Senior Data Scientist (Advanced Insights)

Position Summary

The Senior Data Scientist (Advanced Insights) is responsible for transforming operational data into actionable

insights that improve business performance. The role owns end-to-end delivery: problem framing, dataset and

metric design, dashboard development, and the implementation of GenAI-enabled solutions that sit on top of

governed enterprise data. The successful candidate is delivery-oriented, collaborative, and capable of shipping

reliable solutions that are adopted by business users.

Key Responsibilities

I) Business & Decision Analytics

Partner with stakeholders to identify high-impact opportunities where analytics and GenAI improve

decision-making and process performance.

Translate business needs into analytical questions, KPI definitions, reporting requirements, and GenAI

user stories.

Manage a portfolio of deliverables with clear success measures and measurable outcomes.

II) Analytics Engineering & Metric Governance

Extract, clean, transform, and integrate data from multiple systems to produce trusted analytics

datasets.

Design and maintain analytics data models (e.g., star schema / subject-area models) to support

reporting and retrieval.

Define auditable metric logic and ensure consistency, data quality, and traceability of key measures.

II) Dashboards, Reporting & Data Storytelling

Design and develop operational dashboards and reports that clearly communicate performance, trends,

and exceptions.

Present insights in business language with clear implications and recommended actions.

Iterate with users to improve usability, clarity, and adoption.

IV) GenAI Solutions (RAG, Chat, Structured Extraction)

Design and implement GenAI workflows including RAG pipelines (ingestion, chunking, embeddings,

retrieval, prompting).

Build document Q&A and structured extraction solutions (documents fields/JSON) with validation

and post-processing.

Build lightweight API services (e.g., FastAPI/Flask) to enable integration with internal systems and

workflows.

V) Mentoring & Standards

Mentor junior analysts / team members on problem framing, SQL, data modelling, dashboard best

practices, and GenAI evaluation hygiene.

Define and promote templates and standards for KPI definitions, dashboards, documentation, and

evaluation.

Required Qualifications

68+ years of experience in data analytics, BI, data science, data engineering, or adjacent roles with

end-to-end delivery ownership.

Strong quantitative reasoning and ability to translate ambiguous business problems into structured

analyses and decisions.

Demonstrated proficiency in:

o SQL and relational databases (e.g., PostgreSQL or similar)

o Python for data manipulation and analysis

o Dashboarding and data storytelling fundamentals (tool-agnostic)

Practical experience delivering at least one of the following in a production or near-production setting:

o Retrieval-Augmented Generation (RAG) using a vector database

o LLM-based structured extraction into JSON / schemas

o LLM integration via APIs into an end-user workflow

Strong communication and stakeholder management skills; able to deliver iteratively in time-boxed

environments.