Data Scientist

• Coach, mentor, and manage a world-class team of Partner Development Managers
• Own and execute a forward-thinking partner strategy focused on innovative ISVs and Data Providers
• Build deep relationships with Snowflake’s sales teams and drive joint sales initiatives
• Collaborate cross-functionally with Product, Industry GTM, Marketing, Operations, and Sales teams
• Track team performance, partner success metrics, and customer impact

Manager, Data Cloud Partners – Manufacturing

• Provides expert support to run analysis and analytical experiments using both structured and unstructured data.
• Evaluates alternate models via theoretical approaches.
• Identifies and exploits data assets, creating new patterns and business value.
• Creates analyses and models to improve customer and operational Key Process Indicators (KPIs).
• Enhances measurable value for the Agency and its customers.
• Builds predictive models and develops advanced algorithms that extract and classify information from large datasets.
• Analyzes data, develops robust statistical and predictive algorithms/models that drive product outputs.
• Identifies meaningful patterns and trends, exposing new business opportunities.
• Translates results into understandable and actionable output that answers real business questions.
• Works with modules such as Acquire to Retire (A2R), Cost Accounting (CA), User Management (UMX), Time & Labor (T&L), Procure to Pay (P2P), Order to Cash (O2C), Budget to Report (B2R), and Custom Application Development (CAD) Support Services.
• Develops data conversion templates, pre/post conversion checklist/steps, data conversion design documents, data conversion status reports, and data conversion migration plans.
• Provides expertise in COOP development and implementation, including disaster recovery planning, testing, support, recovery restoration, and system reconstitution.
• Will analyze financial and ERP data sets.
• Collaborates with hosting provider’s teams, including Systems Engineers, Network Engineers, and Continuity/Disaster Recovery Teams.
• Executes COOP exercises such as tabletop, simulation/remote recovery, and full failover exercises.
• Maintains and enhance the DAI Information System Contingency Plan (ISCP).
• Analyzes and upgrade COOP design to align with software upgrades and technical changes.
• Evaluates new technologies like Oracle Golden Gate for COOP environment re-architecture.

Financial Data Scientist

• Act as a lead data manager for one or more projects.
• Act as primary contact with the sponsor as required and communicate with management regarding all data management activities within their studies.
• Accountable for data management activities on assigned study projects from database start-up through database lock and CSR/submission.
• Serve as the primary data management contact for internal, sponsor and 3rd party vendor teams.
• Responsible for delivering quality data to the sponsors.
• Responsible for reviewing the revenue recognition workbooks with the Project Manager(s) when DM tasks are completed for each month/phase of the project to ensure client payments.
• Create and maintain the Data Management Plan and all other core DM documents (e.g., data review plans, data transfer plans, coding guidelines, CRF completion guidelines, database finalization and freeze/lock, etc.) for assigned projects and ensure they follow UBC SOPs.
• Provide input on study timelines, identify risks, and communicate mitigation strategies to the Project Manager.
• Ensure data management deliverables are met on time.
• Utilize communication, available metrics reports, experience, knowledge, and other resources to efficiently lead and coordinate all data management activities.
• Ensure study team members from different functional areas understand and deliver components affecting data to ensure successful execution of deliverables.
• Provide solutions for data management issues that arise during study conduct.
• Design and develop database build specifications (i.e., CRFs/ePROs, Visit/form configuration specifications, edit check specifications) to meet study data requirements per protocol.
• Develop data cleaning requirements for assigned projects.
• Distribute data review tasks to the data management team on the project.
• Coordinate and participate in all user acceptance testing (UAT) activities of study databases and external vendor systems.
• Develop documents and support Real World Evidence Projects (e.g., Feasibility Questionnaire, Variable Gap).
• Develop, track, analyse and report, as applicable, on data management performance metrics from study start to database lock and provide to project managers, sponsors, etc.
• Responsible for the adhering to project budgets and notifying project teams of the need for potential change order.
• Responsible for the training and support of project-specific DM staff on all data management operations and documents (e.g., CRFs/PROs/COAs/RWE designs, protocol, data cleaning process/query resolution) on an ongoing basis.
• Provide input during the development and/or review of Data Management SOPs, Work Instructions and templates/standards incorporating the industry's best practices and any applicable Regulatory guidelines and requirements.
• Participate in business development activities as required.
• Other duties as assigned by management.

Lead Data Manager

• Act as a lead data manager for one or more projects
• Act as primary contact with the sponsor as required and communicate with management regarding all data management activities within their studies
• Accountable for data management activities on assigned study projects from database start-up through database lock and CSR/submission
• Serve as the primary data management contact for internal, sponsor and 3rd party vendor teams
• Responsible for delivering quality data to the sponsors
• Responsible for reviewing the revenue recognition workbooks with the Project Manager(s) when DM tasks are completed for each month/phase of the project to ensure client payments
• Create and maintain the Data Management Plan and all other core DM documents (e.g., data review plans, data transfer plans, coding guidelines, CRF completion guidelines, database finalization and freeze/lock, etc.) for assigned projects and ensure they follow UBC SOPs
• Provide input on study timelines, identify risks, and communicate mitigation strategies to the Project Manager
• With oversight, ensure data management deliverables are met on time
• Utilize communication, available metrics reports, experience, knowledge, and other resources to efficiently lead and coordinate all data management activities
• Ensure study team members from different functional areas understand and deliver components affecting data to ensure successful execution of deliverables
• Provide solutions for data management issues that arise during study conduct
• Design and develop database build specifications (i.e., CRFs/ePROs, Visit/form configuration specifications, edit check specifications) to meet study data requirements per protocol
• Develop data cleaning requirements for assigned projects
• Distribute data review tasks to the data management team on the project
• Coordinate and participate in all user acceptance testing (UAT) activities of study databases and external vendor systems
• Develop documents and support Real World Evidence Projects (e.g., Feasibility Questionnaire, Variable Gap)
• Develop, track, analyse and report, as applicable, on data management performance metrics from study start to database lock and provide to project managers, sponsors, etc.
• Responsible for adhering to project budgets and notifying project teams of the need for potential change order
• Responsible for the training and support of project-specific DM staff on all data management operations and documents (e.g., CRFs/PROs/COAs/RWE designs, protocol, data cleaning process/query resolution) on an ongoing basis
• Provide input during the development and/or review of Data Management SOPs, Work Instructions and templates/standards incorporating the industry's best practices and any applicable Regulatory guidelines and requirements
• Participate in business development activities as required.
• Other duties as assigned by management

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
• Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
• Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
• Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
• Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Senior AI Data Scientist

• Build and maintain strong relationships with internal and external stakeholders to develop and execute comprehensive project plans aligned with business and product objectives.
• Define project scope, goals, deliverables, success criteria, and business outcomes in partnership with key stakeholders.
• Lead end-to-end delivery of complex IT and Data Platform projects from planning and execution through governance, monitoring, and successful operational handoff.
• Develop and manage integrated project plans, schedules, budgets, resource plans, forecasts, milestones, dependencies, and risk registers.
• Partner with business stakeholders to balance requirements, priorities, costs, resources, and delivery trade-offs while identifying win-win solutions.
• Ensure adherence to project governance standards, raising risks and issues to management and stakeholders as required.
• Apply Agile methodologies to project delivery by coordinating sprints, iterations, and incremental product value delivery.
• Deliver projects within agreed scope, quality, timeline, budget, and customer expectations.
• Identify, assess, and mitigate project risks while developing contingency plans and maintaining comprehensive risk registers.
• Manage project change requests, approvals, documentation, meeting minutes, and decision traceability in accordance with governance standards.
• Oversee third-party supplier engagements, including contract negotiations, performance management, and accountability for contractual obligations.
• Serve as the primary liaison between project teams, business stakeholders, technology teams, and external suppliers.
• Prepare and deliver project status reports, executive updates, and presentations for governance and steering meetings.
• Translate complex technical concepts into clear business language for executive and non-technical audiences.
• Obtain formal customer acceptance of project deliverables and ensure successful project closure.
• Track and maintain project performance metrics to support continuous improvement in delivery quality, demand management, and operational effectiveness.
• Lead, organize, and motivate cross-functional matrix teams comprised of internal resources, application experts, and third-party suppliers.

Senior Project Manager, Data & AI

• Own assigned projects and workstreams from initiation through closeout.
• Develop and maintain project execution plans, schedules, and milestone tracking.
• Drive project progress and accountability across internal teams, vendors, consultants, and external partners.
• Coordinate project activities to ensure alignment with program objectives, timelines, and deliverables.
• Facilitate project meetings, track action items, and follow through to completion.
• Build and maintain project-level schedules aligned to the broader program master schedule.
• Monitor project dependencies and identify impacts to adjacent workstreams.
• Support integration of project schedules into the overall program plan.
• Escalate schedule risks and conflicts proactively while recommending mitigation strategies.
• Coordinate activities across engineering, design, construction, infrastructure, and operational stakeholders.
• Manage communication between project teams, vendors, consultants, and leadership.
• Ensure all parties understand scope, responsibilities, milestones, and expectations.
• Track project budgets, scope, schedules, risks, issues, and progress.
• Maintain accurate project documentation including meeting notes, decision logs, change requests, and status reports.
• Provide regular project updates and executive-ready reporting to Program Leadership.
• Identify project-level risks, constraints, and issues early.
• Drive resolution through collaboration and ownership.
• Develop mitigation plans and corrective actions when needed.
• Escalate issues appropriately while ensuring accountability remains clear.

Project Manager – Data Center Delivery

• Developing effective anomaly detection metrics and monitoring tools to identify potential fraud.
• Analyzing data to detect suspicious activity, identify fraud patterns, and surface emerging trends.
• Partnering closely with cross‑functional teams to share insights and continuously improve fraud mitigation processes.
• Performs analytical tasks on vast amounts of structured and unstructured data to extract actionable business insights.
• Participates in the data gathering, data processing and data mining of large and complex datasets.
• Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
• Assists in the presentation of business insights to management using visualization technologies and data storytelling.

Data Scientist – Enterprise Fraud

• Partner with Trade leaders to solve complex business problems using data, analytics, and quantitative decision-making
• Focus on building and productionizing Agentic AI solutions
• Translate ambiguous business questions into analytical frameworks
• Develop AI-powered tools and workflows
• Influence strategic and operational decisions
• Serve as a trusted advisor to business stakeholders
• Combine hands-on AI engineering skills, strong statistical reasoning, business acumen, and structured problem-solving to identify and act on trade insights
• Emphasize designing and deploying Agentic AI solutions via APIs
• Conduct rapid hypothesis generation, statistical analysis, experimentation, forecasting
• Communicate clear recommendations that drive measurable business outcomes

Senior Data Scientist – Health100 AI

• Partner with Product, Operations, Finance, and other business teams to answer high-impact business questions using data.
• Translate ambiguous business problems into structured analytical approaches and actionable recommendations.
• Develop KPIs, metrics, and performance measurement frameworks.
• Present insights and recommendations to senior leadership in a clear, compelling manner.
• Serve as a trusted analytical advisor to cross-functional stakeholders.
• Design, execute, and analyze A/B tests and other experiments.
• Evaluate product features using statistical techniques and causal inference.
• Identify opportunities to improve customer engagement, conversion, retention, and operational efficiency.
• Build measurement frameworks for new products and initiatives.
• Develop predictive models, forecasting solutions, segmentation models, and optimization algorithms where appropriate.
• Apply statistical and machine learning techniques to solve business problems.
• Collaborate with engineering teams to deploy analytical solutions into production when needed.
• Evaluate model performance and communicate trade-offs to business stakeholders.
• Write complex SQL queries against large-scale datasets.
• Design, build, and maintain executive dashboards, reports, and self-service analytics.
• Develop reliable ETL/ELT pipelines that support reporting and analytical workflows.
• Improve data quality, documentation, and metric consistency across the organization.
• Help establish best practices for analytics engineering and reporting.

Senior Data Scientist

🌐
Worldwide
❌ Jobs You've Hidden
⭐️ Saved Jobs
✅ Applied Jobs
✉️ Email Alerts
👤 Account
404 - Page Not Found
🌐
Worldwide
Built by Lior Neu-ner. I'd love to hear your feedback — Get in touch via DM or support@remoterocketship.com
Search
Search Jobs by country
Search jobs by city
Search jobs by job title
Search entry-level jobs
Search junior-level jobs
Search senior-level jobs
Search jobs by tech stack
Search jobs by contract type
Search remote internships
Search remote part-time jobs
Remote jobs Anywhere in the World
Companies Hiring Anywhere in the World
Companies Hiring Sales People Anywhere in the World
Companies Hiring Software Engineers Anywhere in the World
Resources
Advice
Tips for finding remote jobs
Interview questions and answers
Resume examples
Cover letter examples
Post a job
Affiliates
Privacy policy
Terms of service
Job board SEO course
AI Apply Copilot
OpenClaw job finder
Find jobs using your resume
Jobs by Country
Remote jobs anywhere in the world (Worldwide remote jobs)
Remote jobs United States
Remote jobs Australia
Remote jobs Brazil
Remote jobs Canada
Remote jobs France
Remote jobs Ireland
Remote jobs Germany
Remote jobs Netherlands
Remote jobs Spain
Remote jobs UK
Popular Jobs
Remote data analyst jobs
Remote customer support jobs
Remote executive assistant jobs
Remote marketing jobs
Remote product designer jobs
Remote product manager jobs
Remote project manager jobs
Remote recruiter jobs
Remote sales jobs
Remote software engineer jobs
Jobs by Type
Remote full-time jobs
Remote part-time jobs
Remote contract jobs
Remote internship jobs
Remote entry-level jobs
Remote jobs with no experience required
Remote junior jobs (1-3 years of experience)
Digital nomad jobs
Remote jobs with no degree required
Freelance remote jobs
Temporary remote jobs
Remote jobs hiring now
Stay at home mom jobs

Data Scientist II

• Provides technical oversight and planning, risk management, and emergency management support of all Emory University Office of Information Technology (OIT) critical facilities, data closets, and data center colocations
• Manages ticket audits, performance reviews, and development of Critical Infrastructure Facility Specialist
• Collaborates with internal partners to provide design, engineering, and implementation support for security, electrical, generator and environmental issues
• Identifies system opportunities and develops corrective actions to improve efficiency and reliability
• Plans and implements green data center strategies to reduce the total cost of ownership
• Manages equipment installations, cross-connects, electrical requests, relocations, removals and maintenance schedules
• Ensures supporting processes are in place and followed
• Identifies necessary people, processes, metrics, and technology improvements in pursuit of best-in-class performance, uptime, and reliability
• Participates in disaster recovery planning and testing
• Manages and provides oversight for all Emory OIT data center and data closet projects

Manager, Critical IT Facilities – Data Center Systems

• Responsible for the technical delivery both as a project manager and site lead for mission critical commissioning projects with minimal supervision while completing assignments that require independent judgement and evaluation.
• Maintain effective communication with clients, contractors and other project team members.
• Develop and maintain client relationships.
• Manage project specific commissioning plans, commissioning procedures, checklists, test scripts, and schedules for Level 1 through Level 5 commissioning activities.
• Lead onsite teams to perform functional performance tests on mechanical, plumbing, and electrical systems.
• Provide project management and coordination to meet client expectations for technical excellence, budget and schedule.

Data Center Commissioning Project Manager

• Develop and maintain strong relationships with clients, understanding their aftermarket needs and objectives.
• Act as the primary point of contact for key accounts, providing exceptional customer service and support.
• Conduct regular meetings with clients to review aftermarket performance, address concerns, and identify opportunities for improvement.
• Monitor and analyze the performance of data center aftermarket services and solutions provided to clients.
• Utilize data analytics and reporting tools to track key performance indicators (KPIs) and service levels.
• Identify trends, areas for improvement, and opportunities for optimization to enhance client satisfaction and operational efficiency.
• Address and resolve client issues or concerns promptly, coordinating with internal teams to ensure effective solutions.
• Manage escalations and work to resolve complex or critical problems that impact client performance or satisfaction.
• Develop and implement strategic account plans to align with clients’ business goals and requirements.
• Propose and execute initiatives to enhance service delivery, drive innovation, and support clients’ evolving needs.
• Prepare and present regular performance reports and reviews to clients, highlighting achievements, challenges, and future plans.
• Maintain accurate and up-to-date documentation related to account performance, service levels, and client interactions.
• Ensure that services and deliverables are provided in accordance with contractual agreements and service level agreements (SLAs).

Program Manager – Data Centers

• Design and implement statistical and machine learning models for time-series forecasting, anomaly detection, and asset health scoring across utility networks.
• Build and maintain end-to-end ML pipelines on Databricks from feature engineering and model training to validation, deployment, and monitoring in production.
• Apply classical statistical methods (GLMs, GAMs, mixed-effects models, Bayesian inference) alongside modern ML techniques (ensemble approaches, network analysis, neural networks) to solve grid operations problems.
• Develop predictive maintenance and degradation models for utility infrastructure using telemetry and SCADA data at scale.
• Translate ambiguous business problems into well-defined modeling problems with appropriate statistical frameworks - e.g., knowing when a LM/GLM is sufficient and when gradient boosting or deep learning is warranted.
• Implement model monitoring, drift detection, and automated retraining workflows to maintain model performance over time.
• Contribute to load forecasting, demand response optimization, and outage prediction systems.
• Ensure model interpretability and explainability for utility stakeholders and regulatory compliance.
• Contribute to internal knowledge-sharing on statistical best practices.

• Lead the engineering, development, and delivery of enterprise data solutions utilizing Microsoft Fabric.
• Engineer scalable and reusable data pipelines, data products, semantic models, and platform capabilities that support business objectives.
• Design and implement solutions aligned with Medallion Architecture and Data Mesh principles to ensure scalability, maintainability, and domain ownership.
• Establish and promote engineering standards, development practices, and implementation patterns across the Data Solutions organization.
• Serve as the lead technical advisor for capabilities, evaluating technologies and providing recommendations to the Data Solutions Manager regarding platform strategy and technical direction.
• Engineer proof-of-concepts and prototype solutions to evaluate emerging technologies, capabilities, and business opportunities.
• Partner with business and technology stakeholders to translate requirements into engineered solutions that deliver measurable business value.
• Drive technical execution across initiatives by resolving complex engineering challenges and ensuring successful solution delivery.
• Champion engineering excellence through automation, performance optimization, code quality, testing, and operational reliability.
• Ensure solutions are secure, scalable, supportable, and aligned with enterprise standards and operational requirements.
• Engineer, develop, and optimize data ingestion, transformation, and orchestration pipelines using Microsoft Fabric Data Factory, Azure Data Factory, Fivetran, APIs, and related technologies.
• Build and maintain reusable ELT/ETL frameworks, engineering accelerators, and integration patterns.
• Develop and optimize data solutions supporting enterprise reporting, analytics, AI, and operational workloads.
• Engineer integrations across SQL Server, Oracle, SaaS applications, APIs, and other enterprise platforms.
• Troubleshoot and resolve complex data, platform, performance, and integration issues.
• Implement monitoring, observability, and data quality controls to ensure platform reliability and trustworthiness.
• Continuously improve performance, scalability, maintainability, and cost efficiency of data solutions.
• Contribute directly to sprint commitments through engineering, development, testing, deployment, and support activities.
• Lead technical implementation efforts for strategic Data Solutions initiatives.
• Participate in backlog refinement, solution estimation, technical reviews, and release planning activities.
• Review and contribute code to ensure adherence to engineering standards and best practices.
• Support production environments through root cause analysis, incident resolution, and continuous improvement initiatives.
• Drive automation and operational efficiency across development, deployment, monitoring, and support processes.
• Mentor engineers through technical coaching, code reviews, pair programming, and collaborative solution development.
• Lead by example through direct engineering contribution and delivery ownership.
• Promote engineering best practices, reusable patterns, and modern development methodologies.
• Assist in onboarding and developing engineers through knowledge sharing and technical guidance.
• Foster a culture of accountability, continuous learning, innovation, and engineering excellence.

Data Solutions Tech Lead

• Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
• Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
• Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.
• Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.
• Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models.

Data Scientist II, ML Infrastructure

• - Lead enterprise Data Governance engagements for healthcare organizations.
• - Serve as a trusted advisor to executive leadership, business stakeholders, and technical teams.
• - Facilitate governance councils, steering committees, executive workshops, and strategic planning sessions.
• - Guide clients in developing governance strategies that align with organizational priorities and measurable business outcomes.
• - Develop and grow long-term client relationships by identifying opportunities to expand governance capabilities beyond initial project engagements.
• - Lead the integration of Data Governance across the entire data lifecycle—from platform configuration and data acquisition through data product development, analytics execution, AI initiatives, and ongoing operational management.
• - Bridge development teams, architects, analysts, and business stakeholders to ensure governance is embedded into enterprise processes rather than operating as a separate function.
• - Champion governance as an enabler of innovation by integrating governance practices into technology implementation, analytics delivery, and organizational decision-making.
• - Design and establish enterprise Data Governance operating models, including governance structures, decision rights, stewardship organizations, and accountability frameworks.
• - Lead enterprise Data Governance maturity assessments and develop executive roadmaps aligned with organizational strategy.
• - Develop governance policies, standards, processes, and decision-making frameworks.
• - Define data ownership, stewardship, and accountability models across business and technical teams.
• - Embed governance best practices into organizational processes to improve consistency, trust, efficiency, and decision-making.
• - Partner with business, healthcare operations, analytics, and technology leaders to prioritize governance initiatives supporting enterprise transformation.
• - Develop governance strategies that enable trusted analytics, AI, regulatory compliance, and enterprise data management initiatives.
• - Establish governance success metrics and maturity measures.
• - Build business cases demonstrating the value of governance investments.
• - Lead enterprise Data Governance maturity assessments and executive stakeholder interviews.
• - Evaluate governance capabilities across people, process, technology, organizational adoption, and business alignment.
• - Develop executive summaries, strategic recommendations, and multi-year governance roadmaps.
• - Build trusted relationships with executive leaders and become a strategic advisor for enterprise data initiatives.
• - Translate business strategy into scalable governance programs that drive organizational adoption.
• - Bridge business, healthcare operations, analytics, and technology teams to create governed processes throughout the enterprise data pipeline.
• - Deliver measurable improvements in trust, consistency, operational efficiency, regulatory readiness, and business value.
• - Foster collaboration across business, technical, and operational teams.
• - Mentor consultants while contributing to the continued growth of Prominence's Data Governance practice.
• - Balance strategic leadership with hands-on execution.
• - Continuously evaluate emerging technologies, governance practices, and industry trends to improve client outcomes.
• - Demonstrate curiosity, adaptability, humility, and a passion for continuous learning.

Data Governance Lead

• Lead the design, development, and enhancement of forecasting models to improve prediction accuracy and business outcomes.
• Build and deploy advanced Deep Learning models for large-scale structured and unstructured datasets.
• Develop Generative AI / Explainable AI solutions to provide transparent and interpretable insights from predictive models.
• Analyze healthcare datasets including claims, patient, provider, operational, or clinical data.
• Collaborate with business stakeholders, product teams, and engineering teams to translate business challenges into scalable AI solutions.
• Monitor model performance, retrain models, and optimize algorithms for production environments.
• Mentor junior data scientists and provide technical leadership across the project.
• Ensure compliance with healthcare data privacy and governance standards.

Lead Data Scientist – Healthcare

• Deliver generative AI features: design, fine‑tune, and evaluate LLM/LMM applications (prompting, RAG, safety, and evals) that ship to production.
• Build agentic AI workflows: plan and execute multi‑step tasks with tool use, memory, and orchestration; integrate with internal APIs and data sources.
• Build & ship models: Develop, deploy, and scale machine learning models; own full lifecycle from experimentation to production, monitoring, and iteration.
• Integrate analytics: Embed advanced analytics into applications and systems to enhance functionality and decision-making.
• Deep data analysis: Analyze large, complex datasets to extract insights that inform strategy and product direction.
• Raise the bar on rigor: Apply regression, tree-based methods (Random Forest, Boosting), text mining/NLP, neural networks, and clustering with strong statistical validation.
• Optimize pipelines: Improve performance and reliability of ML pipelines and cloud integrations across AWS, Google Cloud, and Azure.
• Mentor & lead: Guide junior teammates; enforce best practices for experimentation, documentation, and reproducibility.
• Partner cross-functionally: Work closely with product, engineering, and business stakeholders to align initiatives with outcomes and timelines.

Data Scientist jobs in United States

See all jobs on JobTailor

Manager, Data Cloud Partners – Manufacturing

Financial Data Scientist

Lead Data Manager

Lead Data Manager

Senior AI Data Scientist

Senior Project Manager, Data & AI

Project Manager – Data Center Delivery

Data Scientist – Enterprise Fraud

Senior Data Scientist – Health100 AI

Senior Data Scientist

Data Scientist II

Manager, Critical IT Facilities – Data Center Systems

Data Center Commissioning Project Manager

Program Manager – Data Centers

Data Scientist

Data Solutions Tech Lead

Data Scientist II, ML Infrastructure

Data Governance Lead

Lead Data Scientist – Healthcare

Lead Data Scientist