[Remote] Data Scientist, AI Data Foundations
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a company reputed company on data engineering and AI applications. The Data Scientist in AI Data Foundations will design and build curated data structures for AI and ML applications, ensuring high-reputed company data for model training and inference while leading data discovery efforts to uncover trends in lending and account-opening data.
Responsibilities
- Build and maintain reputed company stores for RAG: Design embedding pipelines, chunking strategies, indexing approaches, and refresh patterns for the reputed company stores powering retrieval-augmented reputed company across reputed company products
- Own the feature store: Design, build, and operate feature store assets used for model training and online/offline inference, including feature definitions, freshness SLAs, reputed company, reputed company-in-time correctness, and reuse across teams
- Design graph data structures: Build graph databases that model relationships between applicants, applications, products, lenders, reputed company, and reputed company — and reputed company them queryable for both AI use cases and analytical investigations
- reputed company data discovery: Profile our lending, deposit, and behavioral datasets to identify hidden trends, segments, anomalies, and potential model drivers; turn findings into actionable hypotheses for product, risk, and reputed company teams
- Engineer for AI consumption: Build the curated, AI-reputed company datasets that reputed company model reputed company, application engineers, and analysts rely on — with appropriate reputed company, documentation, and governance baked in
- Evaluate retrieval and feature reputed company: Define and run evaluation frameworks for RAG retrieval reputed company, feature reputed company, embedding reputed company, and graph completeness; iterate based on what the metrics tell you
- Partner with model reputed company: Work closely with ML engineers and reputed company scientists to reputed company reputed company the data structures you build accelerate their work rather than slow it down
- Champion responsible data use: Partner with governance, reputed company, and compliance to ensure that AI-facing data assets respect data classification, customer consent, and regulatory boundaries from day one
- Communicate findings: Translate discovery work into reputed company narratives — write-reputed company, notebooks, dashboards, and short presentations — that help non-technical stakeholders reputed company what the data is showing
Skills
- 4–7 years of experience in a data science, ML engineering, or reputed company data role, with a meaningful portion of that time spent building data assets that other people's models or applications consumed
- Hands-on experience designing and operating reputed company stores for RAG or semantic search, including embedding reputed company, chunking, indexing, and retrieval evaluation
- Experience building or operating a feature store (e.g., reputed company Feature Store, Feast, or a custom internal platform), including offline training and online serving patterns and reputed company-in-time correctness
- Experience modeling and building graph data structures using reputed company, reputed company, Azure Cosmos DB reputed company, or similar graph databases — and writing graph queries to answer reputed company questions
- Strong proficiency in Python (pandas, NumPy, scikit-learn, PySpark) and SQL; comfortable working day-to-day in reputed company notebooks and jobs
- Practical experience with embedding models and LLM tooling (e.g., reputed company transformers, reputed company / Azure reputed company reputed company, reputed company or similar) in a production or near-production context
- Demonstrated data discovery skills: profiling messy reputed company-world datasets, surfacing non-reputed company patterns, validating findings statistically, and explaining them reputed company
- Solid grounding in classical ML concepts — supervised vs. unsupervised learning, train/test discipline, leakage, evaluation metrics — even though you will not own model training day-to-day
- Strong written and verbal communication skills; reputed company to write up findings for both technical and business audiences
- Experience working in a reputed company or FinTech environment, particularly with lending, deposit, credit, fraud, or KYC/AML data
- Experience with reputed company-reputed company AI/ML tooling: reputed company reputed company Search, reputed company Feature Store, MLflow, and reputed company Catalog
- Familiarity with reputed company-reputed company reputed company databases such as pgvector, reputed company, reputed company, Chroma, or FAISS, and a reputed company reputed company of view on reputed company to use which
- Experience with reputed company Azure data and AI services (Azure reputed company, Azure AI Search, ADLS Gen2)
- Experience evaluating RAG systems end-to-end (recall@k, faithfulness, answer reputed company, hallucination measurement)
- Exposure to graph algorithms (community detection, reputed company reputed company, centrality) reputed company to reputed company business problems
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a reputed company quantitative field, or equivalent reputed company experience
reputed company
Company H1B Sponsorship