Job Description Advanced Data Scientist Location

Bangalore, India

Role Overview

We are looking for a Advanced Data Scientist who can own end‑to‑end data science and machine learning solutions, from problem formulation to production deployment.
This role requires a strong blend of machine learning expertise, data engineering, MLOps, cloud platforms, and technical leadership.

You will work closely with product, engineering, and business stakeholders to design scalable data and ML systems that drive measurable business impact.

Experience

8–12+ years

Key Responsibilities Data Science & Machine Learning

• Translate business problems into data science and ML solutions
• Perform advanced EDA, feature engineering, and model development
• Build and optimize:
• Classical ML models (regression, classification, tree‑based models)
• Time‑series, anomaly detection, and recommendation systems
• Develop and fine‑tune deep learning models using Py Torch / Tensor Flow
• Design and evaluate experiments (A/B testing, statistical validation)

GenAI, NLP & LLM Solutions

• Build NLP and GenAI applications using modern LLMs
• Implement RAG pipelines, prompt engineering, and vector search
• Integrate LLMs using OpenAI / Azure OpenAI APIs
• Evaluate model quality, latency, and cost for production LLM systems

Data Engineering & Pipelines (Good to Have)

• Design and build scalable data pipelines for batch and streaming use cases
• Work with distributed processing frameworks like Apache Spark
• Orchestrate workflows using Airflow / Dagster / Prefect/ Azure Data Factory / Databricks
• Handle real‑time data using Kafka or cloud‑native streaming services
• Ensure data reliability, quality, and performance at scale

MLOps, Deployment & Production

• Own the full ML lifecycle: experimentation → training → deployment → monitoring
• Implement model versioning, reproducibility, and CI/CD pipelines
• Deploy models using REST APIs or batch inference pipelines
• Monitor model performance, drift, and data quality in production
• Work with Docker and Kubernetes for scalable deployments

Cloud & Platform Engineering

• Build solutions on AWS / Azure / GCP (at least one in depth)
• Work with cloud data platforms like Databricks, Snowflake, Big Query
• Optimize system performance and cloud costs
• Ensure security, access control, and compliance best practices

Architecture, Collaboration & Leadership

• Design end‑to‑end data and ML architectures
• Make tradeoffs between batch vs streaming, cost vs performance
• Mentor junior data scientists and review code and models
• Set data science and ML best practices across teams
• Communicate insights clearly to technical and non‑technical stakeholders

Required Skills & Qualifications Core Technical Skills

• Strong proficiency in Python and advanced SQL
• Solid foundation in statistics, probability, and linear algebra
• Hands‑on experience with XGBoost, LightGBM
• Experience with Py Torch or Tensor Flow Data Engineering (Good to have)
• Strong experience with Spark / Py Spark
• Pipeline orchestration using Airflow or similar tools
• Experience with relational, NoSQL, and analytical databases
• Understanding of data lakes and warehouse architectures

MLOps & DevOps (Optional)

• Experience with MLflow, DVC, or W&B
• Model deployment using FastAPI
• Containers and orchestration: Docker, Kubernetes
• CI/CD and monitoring tools

Cloud Platforms

• Deep expertise in at least one cloud provider:
• AWS, Azure, or GCP
• Experience with managed ML and data services

Preferred / Nice‑to‑Have

• Experience with LLM frameworks (Lang Chain, Llama Index)
• Vector databases (FAISS, Pinecone, Weaviate)
• Streaming frameworks (Flink)
• Knowledge of data governance, privacy, and compliance
• Experience leading cross‑functional technical initiatives

Machine Learning Algorithms & Techniques (Hands‑On)Supervised Learning

• Linear Models
• Linear Regression
• Logistic Regression
• Regularization (L1, L2, Elastic Net)
• Tree‑Based Models
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, Cat Boost)
• Clustering Techniques
• K‑Means
• Hierarchical Clustering
• DBSCAN
• PCA (feature reduction)
• t‑SNE / UMAP (visualization & analysis)

Dimensionality Reduction Time Series & Forecasting (Basic–Intermediate)

• Statistical forecasting:
• Moving averages
• ARIMA / SARIMA (conceptual + basic use)
• ML‑based forecasting using regression and tree‑based models

Model Evaluation & Optimization

• Cross‑validation techniques
• Hyperparameter tuning (Grid Search, Random Search)
• Bias–variance tradeoff
• Handling class imbalance
• Selection of appropriate evaluation metrics