# Senior Data Scientist (US) at Lynx Analytics

> United States · Senior · Analytics / Data science · open. Source: https://alderhq.app/jobs/greenhouse%3Alynxanalytics%3A8470862002 (read 2026-11-10).

## Facts (observed)

- Company: [Lynx Analytics](https://alderhq.app/companies/lynx-analytics)
- Location text: New York, USA; Philadelphia, Pennsylvania, United States; San Francisco, California, United States; New York; Pennsylvania; San Francisco
- Where it can be done from (inferred): United States (evidence: "Location: New York, USA; Philadelphia, Pennsylvania, United States; San Francisco, Califor")
- Posted: 2026-03-19 (235 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/lynxanalytics/jobs/8470862002
- Skills listed (inferred): Python, AWS, GCP, Azure, Docker, SQL, PyTorch, TensorFlow, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (New York, n 42): p25 $144k, median $180k, p75 $202k

## Description (the employer's text)

### 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 RESPONSIBILITIES

### Solution 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).
- Git and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred.
- Hands-on experience designing and building agentic LLM applications - tool calling, multi-step orchestration, and state management - using at least one modern framework (e.g., LangGraph, Pydantic AI, AWS Bedrock AgentCore, Google ADK, or the OpenAI Agents SDK), beyond simple prompt-and-response use of LLM APIs.
- Preferred: practical depth in one or more of MCP-based tool integration, RAG and embedding pipelines (including vector stores), model fine-tuning and RL-based post-training, and LLM guardrails and evaluation (e.g., Ragas, DeepEval, Langfuse, or similar).
- Experience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred.
- Comfortable working with very large, complex datasets residing in different data stores and formats.
- Excellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders.
- Demonstrated leadership potential and the presence to guide junior team members and represent the company with clients.
- Nice to have:
- Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv).
- Experience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling).

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Alynxanalytics%3A8470862002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.