Data Scientist

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases
• Build production-grade systems that combine predictive, causal, and generative AI capabilities
• Turn complex data into actionable insights, automated decisions, and intelligent workflows
• Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI
• Collaborate closely with data engineers, ML engineers, analysts, and platform teams

Senior Data Scientist

• Partner with Product and BI to turn business goals into well-scoped ML problems with clear success criteria and guardrails
• Build and productionize models for segmentation, churn, LTV, and recommendations
• Design evaluation frameworks including offline benchmarks and online feedback loops to ensure models are measured, not assumed
• Collaborate with Engineering on production design considerations like latency, payload structure, fallbacks, and failure modes
• Monitor model performance over time, detect drift, and own retraining and iteration cycles
• Use product telemetry and user feedback to drive continuous model and feature improvements
• Work alongside AI coding agents to accelerate development (pipelines, tests, refactoring, exploration) while maintaining human oversight
• Communicate results, trade-offs, and recommendations clearly across technical and non-technical teams

• Responsible and manages all aspects of the clinical trial data management process
• Ensure that clinical data management deadlines are met with quality
• Oversee project data entry process including development of data entry guidelines
• Conduct database build UAT and maintain quality controlled database build documentation
• Perform quality control of data entry
• Train clinical research personnel on the study specific CRF
• Review and query clinical trial data according to the Data Management Plan
• Liaise with third-party vendors such as external data and EDC vendors in a project manager capacity
• Identify and troubleshoot operational problems and issues
• Participate in the development and maintenance of Standard Operating Procedures (SOPs)

Clinical Data Manager II/Senior Clinical Data Manager

• Primary Data Management (DM) contact (Lead DM role) for assigned clinical project(s) / program(s), ensuring that there is back-up, continuity, responsiveness, and that tasks are performed in a timely manner. May support another Lead DM as a back-up and/or team member. May include DM oversight of sponsor programs.
• Oversee project data entry process including development of data entry guidelines, training, data entry quality and resourcing
• May perform quality control of data entry
• Provide input, assesses and manage timelines. Ensure that clinical data management deadlines are met with quality. Assess resource needs for assigned projects, as needed.
• May develop CRF specifications from the clinical study protocol and coordinate the review/feedback from all stakeholders
• Conduct database build UAT and maintain quality controlled database build documentation. Oversee overall quality of the clinical database.
• May specify requirements for all edit check types e.g. electronic, manual data review, edit checks, etc. Oversee development of the edit check specifications and manual data review specifications
• Responsible for creating, revising, appropriate versioning and maintaining data management documentation. Oversee completeness of data management documentation for the Trial Master File.
• Train clinical research personnel on the study specific CRF, EDC, and other project related items as needed.
• Review and query clinical trial data according to the Data Management Plan
• Perform line listing data review based on the guidance provided by the sponsor and/or Lead DM
• Run patient and study level status and metric reporting
• Perform medical coding of medical terms relative to each other in order to ensure medical logic and consistency
• Coordinate SAE/AE reconciliation
• Liaises with third-party vendors such as external data and EDC vendors in a project manager capacity in support of timelines and data-related deliverables
• May assist with SAS programming and quality control of SAS programs used in the Data Management department
• Identify and troubleshoot operational problems, issues, obstacles, and barriers for studies based on metrics data, audit reports, and input from project team members and other stakeholders
• May assist in reviewing and providing feedback on protocols, Statistical Analysis Plans (SAP) and Clinical Study Reports (CSRs), if required by the project
• Participate in the development and maintenance of Standard Operating Procedures (SOPs) and corresponding process documentation related to data management and database management activities
• May attend strategy meetings, bid defense prep meetings, bid defenses, capability presentations, potential client engagement meetings
• May review Request for Proposals (RFP), proposals, provide project estimates
• Provide leadership for cross-functional and organization-wide initiatives, where applicable
• Trains and ensures that all data management project team members have been sufficiently trained
• Communicate with study sponsors, vendors and project teams as needed regarding data, database or other relevant project issues
• May present software demonstrations/trainings, department/company training sessions, present at project meetings
• May require some travel
• Perform other duties as assigned

Clinical Data Manager II / Senior Clinical Data Manager

• Design, develop, and deploy machine learning and AI-enabled solutions that support partnering activities such as opportunity prioritization, portfolio intelligence, forecasting, operational insights, and decision support.
• Apply advanced analytics, statistical modeling, machine learning, and emerging AI techniques to solve complex business problems and deliver scalable intelligence capabilities across the partnering ecosystem.
• Lead exploratory data analysis, feature engineering, model selection, training, validation, and performance evaluation for machine learning and AI-enabled solutions.
• Partner closely with business stakeholders and product teams to translate complex business challenges into analytical approaches, machine learning solutions, and scalable intelligence capabilities.
• Collaborate with AI, data, and engineering teams to operationalize AI-enabled capabilities and support the adoption of scalable intelligence solutions across partnering platforms and workflows.
• Design and develop analytics solutions, dashboards, KPIs, and intelligence capabilities that improve visibility into partnering operations, opportunities, and portfolio activities.
• Collaborate with engineering and architecture teams to support operationalization of machine learning and AI-enabled solutions in production environments.
• Communicate analytical findings, model outputs, and AI-driven insights through clear visualizations, presentations, and storytelling.

Data Scientist – Applied AI

• Partner with product teams and business leaders to understand and define Agentic AI use cases
• Collaborate with data scientists across global teams to gather feedback on the agent building experience and translate it into framework improvements
• Shape and drive the evolution roadmap for the Agentic AI framework
• Apply GenAI and Agentic AI techniques to solve real business problems
• Build and maintain resilient, production grade algorithmic and agentic pipelines
• Write clean, well structured code following engineering best practices
• Deepen applied knowledge across machine learning, optimization, statistical modeling, and GenAI

Product Data Scientist

• Take ownership of a defined business domain and its algorithmic needs from problem framing through to deployed solution
• Partner with product, business, and AI engineering teams to automate and integrate models into live applications
• Analyze large scale datasets (think: processing billions of behavioral signals daily) and translate findings into actionable recommendations
• Define and evolve the algorithmic roadmap for your area of ownership
• Apply machine learning, statistical, optimization, and GenAI techniques to real business problems
• Write production grade code following engineering best practices
• Build resilient, maintainable algorithmic pipelines that hold up over time

Data Scientist – Machine Learning, GenAI

• Be part of the Data Science (R&D) team and take data throughout its full lifecycle - research, exploration, preprocessing, development, integration and implementation
• Engage in brainstorming sessions and develop prototypes or proofs of concept that utilize Data Science methods to tackle FinCrime-related challenges
• Collaborate with other departments and clients to understand processes and come up with tailored solutions
• Create automation solutions based on AI agents, Generative AI models, Machine Learning techniques
• Contribute to the development of data science products from technical perspective (application backend, data pipelines) and from sales perspective (pricing, solution architectures, visual representation of the concepts, key value drivers)
• Support junior and mid-level team members through mentoring and technical assistance
• Participate in training, certifications, and conferences (also as a speaker, if you wish)
• Work in an Agile environment alongside backend/frontend/low-code developers, DevOps, and business analysts

• Lead Data Science activities across multiple streams, ensuring consistent methodologies, evaluation standards, and model quality
• Define and implement experimentation frameworks, test protocols, and evaluation strategies for AI features
• Establish best practices for prompt evaluation, feasibility assessments, and iterative model improvement
• Oversee how AI models interact with Neo’s knowledge base to ensure accuracy, relevance, and controlled behaviour
• Guide Data Scientists in refining models based on feature intake requirements and platform constraints
• Collaborate closely with engineering and architecture teams to ensure models are ready for integration into production environments
• Act as the central authority on AI model quality, validation, and alignment across Neo
• Drive continuous improvement of Data Science standards and practices as the platform evolves

Data Science Lead

• Lead the design and discovery phase for GenAI projects: translate business problems into concrete architectures covering model selection, RAG, agents, fine-tuning, and guardrails
• Build complete GenAI solutions end to end, covering data ingestion, retrieval layers, orchestration (LangChain, LlamaIndex, LangGraph), API and backend
• Design RAG pipelines using vector databases, hybrid search, rerankers, query transformation, and evaluation frameworks
• Own prompting strategies, model selection, and fine-tuning approaches including LoRA, QLoRA, and supervised fine-tuning
• Implement safety and governance controls: input/output filters, PII handling, audit logs, human-in-the-loop mechanisms
• Gather technical requirements directly from client stakeholders and produce reliable delivery estimates
• Mentor other data scientists and engineers on GenAI patterns, code quality, and best practices
• Track the GenAI landscape actively and run targeted proof-of-concepts on emerging approaches

Senior Data Scientist, Fullstack

• Partner with Product and Business Teams to understand needs and provide actionable insights
• Develop and implement advanced data science solutions
• Collaborate with cross-functional teams to ensure seamless integration
• Explore cutting-edge methods and trends in Data Science & AI
• Communicate insights and recommendations to management and teams

Senior Data Scientist – Operations

• Design, build, validate, and maintain predictive and prescriptive models across travel, risk, and location domains
• Apply statistical methods, machine learning, and experimentation to improve data accuracy, completeness, and trust
• Develop domain-specific metrics, benchmarks, and risk scoring frameworks
• Translate customer problems and market needs into analytical solutions and data products
• Partner with Product Managers to define value propositions, pricing inputs, and success metrics
• Define data quality rules, validation logic, and anomaly detection mechanisms
• Ensure alignment with privacy, consent, and ethical AI standards in regulated environments
• Support lineage, explainability, and auditability of analytical outputs
• Work closely with Data Engineers to ensure model-ready, reliable data pipelines
• Collaborate with Machine Learning Engineers to operationalise models at scale
• Communicate insights and recommendations clearly to technical and non-technical stakeholders

• Serves as Functional Lead for Clinical Data Science;
• Acts as central steward of clinical data quality;
• Communicates, addresses, troubleshoot and resolves data related questions;
• Coordinates data cleaning activities;
• Develops analytical tools and drives data acquisition plans.

Senior Clinical Data Scientist – Data Management Lead

• From hypothesis to recommendation, you drive the full analytical lifecycle in your pillar.
• You define the right metrics, build the right models and make sure your insights land, not just in a deck, but in decisions.
• Work shoulder-to-shoulder with Product Managers, Engineers, and Senior Leaders to shape strategy.
• You'll translate ambiguous business problems into clear analytical tasks and bring a point of view, not just a chart.
• Lead experimentation across your pillar, designing tests, sizing opportunities, interpreting results, and building a culture of evidence-based iteration.
• Create self-serve dashboards and data tools that empower your teams to answer their own questions.
• Define and standardise metrics so everyone's working from the same source of truth.
• Use AI-assisted workflows, from code generation to automated reporting, to accelerate your output and spend more time on the high-value analytical problems that require your expertise.
• Document your work in our Insights Hub, champion consistent data practices, and actively share learnings across the data team.

Product Data Scientist – 6-month project

• Design, develop, and deploy machine learning models to solve complex business problems in the AdTech domain
• Analyze large datasets to generate actionable insights and improve product performance
• Build and maintain scalable data pipelines using big data tools and frameworks
• Perform data preprocessing, cleaning, feature engineering, and model evaluation
• Collaborate with cross-functional teams including engineers, analysts, and product managers
• Ensure model accuracy, reliability, and scalability through rigorous testing and validation
• Stay updated on emerging data science techniques, tools, and best practices
• Contribute to team discussions, process improvements, and knowledge sharing

See all jobs on JobTailor

Senior Data Scientist

Data Scientist

Clinical Data Manager II/Senior Clinical Data Manager

Clinical Data Manager II / Senior Clinical Data Manager

Data Scientist – Applied AI

Product Data Scientist

Data Scientist – Machine Learning, GenAI

Data Scientist

Data Science Lead

Senior Data Scientist, Fullstack

Senior Data Scientist – Operations

Data Scientist

Senior Clinical Data Scientist – Data Management Lead

Product Data Scientist – 6-month project

Senior Data Scientist – AdTech