{"id":3149778,"url":"https://alion.io/job/stockgro-data-scientist","title":"Data Scientist","company":{"id":2108,"name":"StockGro","domain":"stockgro.club","url":"https://alion.io/company/stockgro","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Claude","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-23T11:35:15Z","employer_posted_date":null,"last_verified_at":"2026-10-23T11:35:15Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Responsibilities: \nBuilding and owning a conversational AI research assistant for retail investors in Indian capital markets, think Claude but specialised for different instruments and macro analysis for non-expert users.\nDesigning multi-agent agentic pipelines with tool-calling, memory management, and multi-turn conversational flows that handle real, messy, incomplete queries from retail users (not just clean, structured prompts from analysts).\nRAG pipeline architecture: source curation, chunking strategy, embedding quality, retrieval tuning, reranking, and citation-grounded responses that retail users can trust.\nIntegrating real-time financial data sources (NSE/BSE feeds, Screener, Tickertape, news APIs, company filings) as live tool-callable data layers, not just static retrieval.\nBuilding the guardrails and evaluation layer: domain scoping, hallucination mitigation, confidence scoring, and monitoring to ensure the system stays accurate and within bounds over time.\nFine-tuning or adapting LLMs where retrieval alone isn't sufficient.\nBuilding ML models for user behaviour, personalisation, and financial insights that feed into the conversational layer.\n\nRequirements: \nUnderstanding of large language models (LLMs) like LLAMA, Anthropic Claude 3 or Sonnet.\nFamiliarity with cloud platforms for data science like AWS Bedrock and GCP Vertex AI.\nStrong proficiency in Python and data science libraries (scikit-learn, TensorFlow, PyTorch).\nSolid understanding of statistical methods, machine learning algorithms, and wealth tech applications.\nExperience in data wrangling, visualisation, and analysis.\nCollaborative mindset and ability to thrive in a fast-paced startup environment.\nSkills\nClaude, LLM, LLaMA, PyTorch, Python, TensorFlow, 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