Data Science Engineer

Designation - Data Science Engineer

Location: Bengaluru

Experience: 4–6 years

Function: AI & Data Science

Role Description: We are seeking a Data Science Engineer to build and deploy production ML models and AI features for our CDP platform. You will work in a small, high-ownership AI & Data Science team — building customer segmentation models, entity resolution algorithms, predictive analytics, NLP capabilities, and LLM-powered automation that directly impact how enterprise clients understand and engage with their customers. This is a hands-on engineering role — you build models that ship to production, not notebooks that stay in research.

Key Responsibilities

• Build and deploy customer segmentation and clustering models (K-Means, DBSCAN, hierarchical) at scale
• Develop entity resolution algorithms — fuzzy matching, blocking strategies, probabilistic scoring — to unify customer profiles across disparate data sources
• Build predictive models — churn prediction, conversion propensity, next-best-action recommendations using classification and regression (XGBoost, Random Forest, logistic regression)
• Design and build LLM-powered features — schema mapping automation, natural language querying, AI-driven insight generation using prompt engineering, RAG pipelines, and structured output extraction
• Build NLP capabilities — text embeddings, semantic similarity, entity extraction, text classification using transformers (BERT or similar)
• Write complex SQL for feature engineering — window functions, sessionization, time-series aggregation, customer behavior features from raw event data on Snowflake/BigQuery
• Integrate ML models into the core platform via APIs (FastAPI) for real-time and batch inference
• Own model lifecycle in production — monitoring, drift detection, retraining, versioning
• Work with data engineering teams to ensure clean, structured training data and feature pipelines

Experience and Skills

• Python ML stack — scikit-learn, Pandas, NumPy, XGBoost. This is 70% of the work.
• LLM / GenAI — prompt engineering, RAG fundamentals, embeddings, vector similarity search, calling LLM APIs (Claude, OpenAI, or similar) with structured outputs. Not fine-tuning — effective use of APIs.
• SQL — complex feature extraction queries on analytical databases. Window functions, sessionization, time-series aggregation, cost-aware query patterns. Not basic SELECT.
• NLP — text embeddings (sentence-transformers or similar), named entity recognition, text classification, semantic search
• Model deployment — FastAPI or Flask, Docker containerization, REST API serving
• Entity resolution / record linkage — fuzzy matching (Levenshtein, Jaro-Winkler), blocking strategies, probabilistic matching across multiple fields
• Model evaluation — precision/recall trade-offs, cross-validation, A/B testing

Good to Have

• LangChain, vector databases (Pinecone, FAISS)
• MLflow or experiment tracking
• Kafka consumers for real-time scoring
• Snowflake ML / BigQuery ML
• Time-series forecasting (Prophet, ARIMA)
• Customer analytics or MarTech platform experience

Pay: ₹1,600,000.00 - ₹2,800,000.00 per year

Benefits

• Commuter assistance
• Health insurance
• Leave encashment
• Provident Fund

Work Location: In person