Data Scientist

• Support the development and implementation of statistical, machine learning, and AI models to improve product development, pricing strategies, and customer experiences.
• Contribute to the design, refinement, and continuous improvement of rule based and analytical decision support logic used in pricing and commercial processes.
• Stay abreast of relevant research and industry developments and help the team prototype and evaluate new data science and AI techniques, including responsible use of generative AI where appropriate.
• Collaborate closely with cross functional teams (data science, data engineering, pricing, and business partners) to identify opportunities where analytics and AI can deliver measurable value.
• Partner with pricing, sales, and business stakeholders to translate commercial policies, objectives, and constraints into analytically sound and explainable solutions.
• Analyze historical and transactional data to calibrate recommendations, validate outcomes, identify edge cases, and assess business impact across markets.
• Communicate findings, recommendations, and tradeoffs through clear documentation, presentations, and visualizations, tailoring insights to both technical and non-technical audiences.
• Follow established practices for data privacy, security, and responsible AI; proactively raise risks, assumptions, and questions and partner with the team to address them.

Junior Data Scientist

• Apply rigorous analytical thinking and modern AI capabilities to design, build, and scale high-impact solutions
• Design, develop, and deploy GenAI-powered internal applications, copilots, and workflow accelerators
• Build reusable AI components, including retrieval pipelines, structured prompting patterns, orchestration workflows, and evaluation harnesses
• Design retrieval strategies that connect LLMs to trusted internal knowledge sources, ensuring grounded and reliable outputs
• Develop and maintain statistical and machine learning models to support automation, optimization, forecasting, and classification use cases
• Implement evaluation and validation frameworks to measure quality, accuracy, and consistency of AI-driven systems
• Partner cross-functionally to identify high-value opportunities for AI enablement across the organization
• Create reusable datasets, feature pipelines, and experimentation frameworks to support iterative development
• Uphold high standards for quality, reliability, and responsible AI practices
• Contribute to peer review processes to ensure technical rigor and maintainability
• Document methodologies, assumptions, and implementation details to ensure transparency and reproducibility

Senior Data Scientist

• Work on well-defined analytical tasks with guidance
• Build strong foundations in data science and analytics
• Deliver high-quality, reliable analytical outputs that support business decision-making
• Contribute to the execution of data-driven initiatives by working on data preparation, analysis, and validation
• Apply statistical and analytical techniques to solve business problems under guidance
• Support the development of scalable and repeatable analytical processes
• Collaborate with cross-functional teams to understand business context and align solutions with organizational goals
• Communicate insights and findings through clear documentation and presentations

Data Science and Analytics Analyst

• Serve as the primary internal and Sponsor contact for all Data Management questions, issues, and escalations related to data collection, cleaning, delivery, reconciliation, timelines, and database lock
• Define, maintain, and communicate the Data Management Plan (DMP) in collaboration with Sponsors and internal Clinical Data Management teams
• Determine and document standard and study‑specific edit checks and data processing guidelines to ensure clean, high‑quality data
• Collaborate with internal teams on study setup requirements, including demographics collection and visit schedule requirements
• Lead the development, review, and finalization of data transfer requirements, including file specifications and approvals
• Coordinate with SAS Programmers for data file creation, edit check development, and validation activities
• Generate and deliver sample, routine, complete, and final data transfers in line with study timelines
• Manage database lock activities with Sponsors to ensure data integrity and deliverable timelines are met
• Act as the primary point of contact for Data Correction Requests (DCRs) and query escalation
• Participate in the review and approval of Data Management components to ensure high‑quality study setups
• Maintain complete and up‑to‑date Data Management documentation, including Data Management Plans, file specifications, data transfer agreements, data correction requests, and Sponsor communications
• Identify out‑of‑scope requests and escalate to the Project Manager for Change Order processing
• Conduct routine status meetings with Sponsors, CROs, and internal teams, including preparation of agendas and meeting minutes
• Support monthly reporting activities by providing Data Management metrics as required
• Deliver high levels of customer satisfaction by meeting commitments, timelines, and quality expectations

Data Manager

• Extract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies.
• Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives.
• Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS.
• Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies.
• Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation.
• Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications.
• Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively.
• Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms.
• Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights.
• Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.

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