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

• 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

• Lead the strategy, development roadmap, and lifecycle of data products and platforms
• Define the roadmap for data solutions
• Collaborate with cross-functional teams
• Ensure data products are scalable, reliable, and user-friendly
• Monitor and improve data quality, reliability, and user adoption of data tools
• Define and track success metrics for data products

Staff Data Product Manager

• Learn the payments data landscape — Explore, profile, and understand transaction, customer, and channel data across payment rails (wire, ACH, RTP/instant) to build the foundation for detection models.
• Build fraud-detection ML models — Develop, train, and validate supervised and unsupervised models (classification, anomaly detection, graph/network analysis) that flag fraudulent payments in batch and near-real-time.
• Develop a fraud typology-driven approach — Understand the major categories of payments fraud — account takeover, authorized push payment (APP)/scams, synthetic identity, business email compromise, money mule/laundering patterns — and map each to detection signals and modeling strategies for how to detect and address them.
• Engineer features and data pipelines — Design and maintain reliable feature pipelines (behavioral, velocity, device, network, and aggregate features) that feed models, partnering with data engineering to move from prototype to production.
• Leverage AI to strengthen detection — Identify opportunities to apply modern AI techniques (e.g., deep learning, embeddings, LLMs for unstructured signals, foundation/graph models) to improve fraud coverage and reduce false positives.
• Build explainable AI (XAI) — Apply model-interpretability methods (SHAP, LIME, counterfactuals, reason codes) so fraud analysts, model risk, and regulators can understand why a payment was flagged.
• Measure and communicate impact — Track model performance (precision/recall, false-positive rate, fraud dollars prevented), and clearly present findings and recommendations to both technical and business stakeholders.
• Own projects from inception to delivery — Partner with fraud SMEs, product, and engineering to take detection ideas from hypothesis through deployment and monitoring.
• Stay current — Follow fraud trends, emerging attack patterns, and advances in ML/AI and responsible-AI practices relevant to the banking industry.
• Grow across the data science domains — Build depth in model science, feature science, and insight science, strengthening core skills in programming, math & statistics, distributed computing, and communicating complex results.

Senior Data Scientist

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.

Associate Director, MedTech Epidemiology, Real World Data Science

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.
• The position will report to the Senior Director, MT EPI-RWDS.

• Design the lab of the future by developing technologies that integrate robotics, analytical instrumentation, and software into cohesive, high-performing solutions
• Deliver world-renowned science by partnering with researchers to understand experimental needs and convert them into scalable digital workflows
• Work at the intersection of multiple disciplines, collaborating with experts in automation, data science, modeling, IT, and the CMC community at large
• Lead high-impact projects from concept through deployment, working across teams and stakeholders to deliver meaningful outcomes
• Continuously improve existing digital technologies and automation platforms to enhance performance, usability, and reliability
• Empower others by providing hands-on partnership, support, troubleshooting, and training to scientists using these technologies

Principal Scientist, DSCS Digital Technologies – Laboratory Automation, Time Series Data Strategy

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity challenges.
• In this role, you will work with our customers to identify areas where advanced statistical techniques can help tackle problems, plan and develop prototype solutions, and build out final products.
• You'll get a chance to work with elite cybersecurity professionals and university faculty to build new technologies that will influence national cybersecurity strategy for decades to come.
• You will co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences.
• Our team works on a wide range of projects. Our current work includes research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems.
• Additionally, we craft metrics and experimental designs for large-scale cybersecurity research programs, develop human-in-the-loop machine learning solutions, and build classifiers to identify security vulnerabilities.

• use advanced statistics, data analytics, machine learning, and artificial intelligence to help government and industry clients research and solve cybersecurity challenges
• work with customers to identify areas where advanced statistical techniques can help tackle problems
• plan and develop prototype solutions and build out final products
• co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences
• engage in research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems
• craft metrics and experimental designs for large-scale cybersecurity research programs
• develop human-in-the-loop machine learning solutions
• build classifiers to identify security vulnerabilities

• Own retailer onboarding from kickoff through go-live.
• Manage weekly scan data submission health across the full retailer base.
• Serve as the primary point of contact for all retailer and CPG partner communications.
• Publish the weekly health dashboard for program performance.
• Coordinate with Scan Data Engineering to ensure technical setup and test submissions are completed.
• Monitor each submission cycle and communicate directly with retailers on action items.

Data Programs Manager

• Be a hands-on scientific and technical leader, leading a portfolio of RWE projects, developing best practices, and mentoring team members.
• Identify common technical requirements for RWE projects, develop and maintain a common suite of tools that enable RWE data scientists to accelerate project delivery.
• End-to-end expertise in RWE studies including conceptualizing research questions, data feasibility, study design, analysis, programming, and interpretation.
• Provide thought leadership and hands-on programming expertise for developing and applying Real-World Data (“RWD”) methodologies to mitigate observed and unobserved biases in the execution of external control arm studies, hybrid control studies, comparative effectiveness analyses, etc.
• Provide thought leadership and hands-on programming expertise to generate actionable insights from post-hoc RCT analyses, observational databases, and literature reviews to support regulatory agency interactions.
• Independently create study protocols, statistical analysis plans, and statistical programming deliverables including analysis-ready data, tables and figures
• Partner with the Data Science Therapeutic Area scientists to conceptualize, lead, shape and deliver Real World Evidence

Senior Principal Scientist, Data Science RWE

• Be a hands-on scientific and technical leader, leading a portfolio of RWE projects, developing best practices, and mentoring team members.
• Identify common technical requirements for RWE projects, develop and maintain a common suite of tools that enable RWE data scientists to accelerate project delivery.
• End-to-end expertise in RWE studies including conceptualizing research questions, data feasibility, study design, analysis, programming, and interpretation.
• Provide thought leadership and hands-on programming expertise for developing and applying Real-World Data (“RWD”) methodologies to mitigate observed and unobserved biases in the execution of external control arm studies, hybrid control studies, comparative effectiveness analyses, etc.
• Provide thought leadership and hands-on programming expertise to generate actionable insights from post-hoc RCT analyses, observational databases, and literature reviews to support regulatory agency interactions.
• Independently create study protocols, statistical analysis plans, and statistical programming deliverables including analysis-ready data, tables and figures.
• Partner with the Data Science Therapeutic Area scientists to conceptualize, lead, shape and deliver Real World Evidence.

• Trusted customer master (the customer lighthouse): a single, governed customer master that includes IDN → member facility → service-line hierarchy, with GPO affiliation (primary and aggregation group), and identifiers integrated with market data
• Fixed, codified metric methodology that serves as one trusted source of truth: a documented, defensible calculation methodology for share-of-wallet, penetration, and addressable/potential value (built on market and GPO data)
• Redefined KPI framework: a smaller, value-based set of leading indicators that measurably changes where RVPs and reps spend their time and holds them accountable to execution
• Initiative tracking linked to EBITDA: a live tracking model where every transformation initiative is linked to its EBITDA impact (calculation owned by Finance) and rolls up to the North Star
• Phase 2 Lighthouse Expansion to the Product Lighthouse: a single, product master data methodology stood up on the same governed model
• Owns: Customer master data methodology and governance: hierarchy logic, GPO affiliation, identity-resolution rules, identifier crosswalks, and integration of market data into the customer lighthouse
• Calculation, methodology, and source of truth for derived commercial metrics (share-of-wallet, penetration, potential/addressable value)
• Design of Commercial KPIs including what is measured, how it is defined, and how it drives focus and accountability
• Initiative tracking model: tracking commercial transformation initiatives and linking each to its EBITDA impact
• The Definitive Healthcare (market-data) relationship, including managing scope changes within an approved budget
• Recommends: Where RVPs and reps should focus from data-driven insights to surface highest value whitespace and penetration opportunities from the trusted data for sales leadership and CVM to act on
• Data, pipeline, and tooling requirements to the ops-data organization focusing on the upstream demand that governs their build
• Sequencing of the lighthouse roadmap (customer → product) and the associated stewardship-ownership decisions
• Informs: Regional and rep-level performance transparency through the redefined KPI set
• Cross-functional and Finance leadership on data-quality gaps, unowned stewardship, and methodology changes that affect reported numbers

Commercial Analytics Lead

• Own the vision, roadmap, and lifecycle of data products
• Partner with IT leaders, analytics teams, and data engineers to translate business needs into clear product requirements and prioritized backlogs
• Drive standardization of key metrics and definitions to enable consistent, decision‑ready insights across IT
• Ensure data products meet enterprise standards for quality, security, governance, and auditability
• Lead cross‑functional delivery from discovery through production, including release planning and ongoing optimization
• Measure and drive adoption, usage, and value realization of data products
• Communicate product strategy, progress, and insights to IT executive stakeholders and senior management
• Continuously improve data product ways of working, leveraging agile and product management best practices

Senior Data Product Manager

• Partner with cross-functional teams to define a neuropsychiatric digital health roadmap that integrates AI/ML (e.g., foundation models), data science, and company priorities across the patient journey, ensuring clinical relevance and patient-centered outcomes.
• Monitor emerging technologies in neuropsychiatry, digital phenotyping, remote assessment, passive sensing, and speech and language analytics to bring forward next-generation solutions for neuropsychiatry R&D.
• Execute the neuropsychiatry-focused digital health strategy by applying AI/ML, modeling, and longitudinal analytics to support patient stratification, endpoint development, and clinical trial optimization, in partnership with disease experts, statisticians, and platforms and clinical teams on study design and validation of digital measures, balancing their informativeness with low clinical burden for patients and study teams.
• Develop and apply sophisticated statistical/ML/AI models using digital health data to generate insights into disease and treatment outcomes.
• Develop multimodal approaches combining digital health data with imaging, blood-based biomarkers, omics, and real-world data to provide a more holistic picture of patients suffering from neuropsychiatric disease.
• Contribute to evidence packages to support regulatory qualification of digital endpoints and tools.
• Ensure validation and compliance with evolving global regulatory and data privacy standards.
• Collaborate with clinical operations to integrate digital health technologies into trial design, working cross-functionally to optimize data capture, integration, and interoperability of digital solutions.
• Partner with regulatory and medical affairs to build external collaborations with academic groups, technology partners, regulators, health authorities, and payers to advance digital health standards and novel endpoints.

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry

• Lead end-to-end AI/ML and GenAI projects, from business problem framing and requirements gathering through data preparation, model development, solution architecture, production deployment, and post-production monitoring and optimization
• Architect and deliver comprehensive ML-based solutions for highly complex, cross-functional projects that address diverse challenges and opportunities across J&J's business segments
• Design, build, and deploy agentic AI systems, including multi-agent orchestration, tool-use frameworks, retrieval-augmented generation (RAG), and autonomous decision-making workflows
• Conduct proof-of-concepts (POCs) and rapidly prototype ML/GenAI-based digital products aligned with business priorities across various segments and functions, translating POCs into production-grade solutions
• Collaborate closely with product management, engineering, and business partners to define product vision, develop technical roadmaps, and ensure AI solutions deliver measurable business value
• Provide technical leadership and mentorship to data scientists and ML engineers; conduct code reviews, model reviews, and establish best practices for the team
• Author and review technical proposals, design documents, and reports; contribute to internal knowledge sharing and external thought leadership in AI/ML
• Stay at the forefront of the AI/ML landscape, continuously evaluate emerging technologies (foundation models, agentic frameworks, multi-modal AI) and drive their adoption to evolve the broader Data Science team's capabilities
• Establish and enforce standards for model governance, responsible AI practices, reproducibility, and documentation across the project lifecycle

Lead Data Scientist

• Partner with cross-functional teams to define a neuropsychiatric digital health roadmap that integrates AI/ML (e.g., foundation models), data science, and company priorities across the patient journey, ensuring clinical relevance and patient-centered outcomes.
• Monitor emerging technologies in neuropsychiatry, digital phenotyping, remote assessment, passive sensing, and speech and language analytics to bring forward next-generation solutions for neuropsychiatry R&D.
• Execute the neuropsychiatry-focused digital health strategy by applying AI/ML, modeling, and longitudinal analytics to support patient stratification, endpoint development, and clinical trial optimization, in partnership with disease experts, statisticians, and platforms and clinical teams on study design and validation of digital measures, balancing their informativeness with low clinical burden for patients and study teams.
• Develop and apply sophisticated statistical/ML/AI models using digital health data to generate insights into disease and treatment outcomes.
• Leverage longitudinal disease modeling, Bayesian methods, and causal inference techniques to inform decision-making.
• Develop multimodal approaches combining digital health data with imaging, blood-based biomarkers, omics, and real-world data to provide a more holistic picture of patients suffering from neuropsychiatric disease.
• Contribute to evidence packages to support regulatory qualification of digital endpoints and tools.
• Ensure validation and compliance with evolving global regulatory and data privacy standards.
• Collaborate with clinical operations to integrate digital health technologies into trial design, working cross-functionally to optimize data capture, integration, and interoperability of digital solutions.
• Partner with regulatory and medical affairs to build external collaborations with academic groups, technology partners, regulators, health authorities, and payers to advance digital health standards and novel endpoints.

Data Scientist jobs in Pennsylvania

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Lead Data Manager

Lead Data Manager

Senior AI Data Scientist

Data Scientist – Enterprise Fraud

Staff Data Product Manager

Senior Data Scientist

Associate Director, MedTech Epidemiology, Real World Data Science

Associate Director, MedTech Epidemiology, Real World Data Science

Principal Scientist, DSCS Digital Technologies – Laboratory Automation, Time Series Data Strategy

Data Scientist

Senior Data Scientist

Senior Data Scientist

Data Programs Manager

Senior Principal Scientist, Data Science RWE

Senior Principal Scientist, Data Science RWE

Commercial Analytics Lead

Senior Data Product Manager

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry

Lead Data Scientist

Senior Manager, R&D Data, Data Science, AI – Digital Health for Neuropsychiatry