Senior Data Scientist (US)
The Senior Data Scientist will lead complex data science engagements, combining traditional statistical modeling with Generative AI and LLM techniques. The role involves hands-on development, client communication with C-level stakeholders, and technical leadership of junior team members.
- Compensation
- Not disclosed
- Location
- New York, USA; Philadelphia, Pennsylvania, United States; San Francisco, California, United States
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data science
- Posted
Skills
Must have
- 8+ years of overall experience in data science
- Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or related field
- Solid grounding in probability theory, statistics, and core data science algorithms
- Strong hands-on proficiency in Python for data analysis, modelling (PyTorch, TensorFlow, or JAX), and productionising code
- Strong SQL and comfort working across common data stores
- Git and GitHub proficiency
- Hands-on experience designing and building agentic LLM applications using modern frameworks
- Experience with at least one major cloud platform (AWS, GCP, or Azure)
Nice to have
- MSc or PhD
- GitHub Actions or equivalent CI/CD experience
- Practical depth in MCP-based tool integration, RAG, embedding pipelines, model fine-tuning, RL-based post-training, and LLM guardrails
- Docker and basic containerised deployment
- Software engineering hygiene (typed Python, pytest, packaging, dependency management)
- Experience shipping LLM applications to production
- Experience in the life sciences industry
Full description
ROLE SUMMARY
We are looking for a Senior Data Scientist to lead complex data science engagements that combine traditional statistical modelling with Generative AI. You will work hands-on with very large datasets across disparate systems and formats, translate ambiguous business problems into rigorous analytical solutions, and present those solutions clearly to C-level stakeholders. This is a delivery-first role with a fast track into technical leadership: alongside your own project work, you will help guide junior data scientists and shape how Lynx builds and ships data science solutions.
KEY RESPONSIBILITIESSolution Design & Delivery
- Design and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques.
- Work hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation.
- Apply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation.
Client Communication & Leadership
- Present results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives.
- Lead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members.
- Partner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations.
Knowledge Building
- Create reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work.
- Participate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice.
SKILLS, QUALIFICATIONS AND EXPERIENCE
- 8+ years of overall experience in data science, with a track record of leading analytical workstreams independently.
- Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred.
- Solid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management.
- Strong hands-on proficiency in Python for data analysis, modelling (PyTorch, TensorFlow, or JAX), and productionising code.
- Strong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases).