Role & responsibilities

significant

Candidates with experience limited to NLP, GenAI, LLM, RAG, Deep Learning, Translation AI, or Forecasting but without hands-on Data Governance, Data Stewardship, Reconciliation Controls, and Data Quality Monitoring should not be considered

- Product/application building team, not the operational team doing day-to-day quality checks

- Building and consolidating fragmented data quality tools (macros, Excel, legacy apps) into a strategic platform

- Candidate will extend an existing product, not build from scratch

- Day-to-day work:

- Writing Python-based rules and logic for quality checks

- Writing complex SQL queries

- Data analytics and testing of solutions

- Anomaly detection solutions (e.g. Python-based or LLM-assisted)

- Consolidating legacy/manual tools into the strategic platform

Must-Have Skills and Experience

- Python: expert level (mandatory,



coding test included in technical round)

- SQL: complex query writing (mandatory)

- Experience: 5 to 7 years total as a data scientist

- Core profile: data scientist background, not just a Python coder

- Big data: Spark preferred; Hadoop or similar acceptable

- Cloud: Azure preferred; AWS also acceptable (migration from AWS to Azure underway)

- Dashboard: Power BI or similar (Tableau not used)

- Machine learning and anomaly detection: beneficial, may be required depending on solution direction

- Data governance: not mandatory, but expected from experienced candidates

- Domain: fintech background not required; general data experience sufficient

- Data relates to equities, derivatives, commodities, but deep financial knowledge not needed

📌 Data Scientist (Bengaluru)
🏢 RiDik
📍 Bengaluru