Data Scientist

We are looking for a Data Scientist to lead initiatives across machine learning, deep learning, and generative AI domains, delivering impactful solutions aligned with business objectives. This position will be full-time and hybrid Bengaluru

What You’ll Do

• Define and drive the technical direction for ML, Deep Learning, LLM, and Generative AI initiatives aligned with business goals

• Own end-to-end solution design decisions, including architecture, modeling approach, and deployment strategy

• Evaluate emerging AI technologies and recommend pragmatic adoption based on feasibility, scalability, risk, and ROI

• Act as a technical authority on trade-offs between model complexity, performance, cost, and interpretability

• Design, build, evaluate, and deploy supervised and unsupervised ML models, deep learning models, and NLP/LLM-based solutions

• Apply strong fundamentals in statistics, experimentation, and validation to ensure robustness and reliability

• Demonstrate judgment in choosing simple vs. complex approaches based on business context

• Architect and implement production-grade ML pipelines including data ingestion, preprocessing, feature engineering, model training, validation, deployment, and serving

• Partner with Data Engineering and Platform teams to build scalable, cloud-native ML systems in AWS, Azure, or GCP

• Ensure best practices around model versioning, observability, lineage, and reproducibility

• Adhere to data governance, security, privacy, and compliance standards

• Design and review logical and physical data models to support analytics and ML workloads

• Influence data architecture decisions to ensure data quality, performance, and reusability

• Collaborate closely with Data Engineering teams on schema design and data readiness for ML

• Hands-on experience with Databricks and Lakehouse architectures including Delta Lake, Auto Loader & Pipelines, Feature Store, and Unity Catalog

• Optimize ML and data workloads for performance, scalability, and cost efficiency

• Define best practices for collaborative development using notebooks, repos, and CI/CD workflows

• Build ML-powered applications and tools to expose insights and models to users and downstream systems

• Develop applications using frameworks such as Django, FastAPI, Streamlit, or Dash

• Design and implement REST APIs for model inference and integration

• Partner with Engineering teams to ensure applications meet performance, security, and deployment standards

• Design and model data in graph databases such as Neo4j, Amazon Neptune, Azure Cosmos DB, or similar platforms

• Build and optimize graph traversal queries for applications like recommendation systems, fraud detection, knowledge graphs, and lineage tracking

• Integrate graph databases with ETL/ELT pipelines, APIs, and cloud data platforms

What You'll Need

Required:

• Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field

• 12–15 years of experience in Data Science, Applied ML, or AI-driven product development

• Proven track record of owning large-scale, business-critical ML/AI solutions

• Experience working in environments with high ambiguity and cross-functional dependencies

• Strong expertise in machine learning, statistical modeling, deep learning, neural architectures, NLP, and Generative AI systems

• Proficiency in Python and SQL

• Experience with TensorFlow, PyTorch, and modern ML frameworks

• Hands-on experience with Databricks including Delta Lake, Feature Store, MLflow, Model Registry, and Model Serving

• Familiarity with cloud environments such as AWS, Azure, or GCP

Physical Demands

• Ability to safely and successfully perform the essential job functions

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings

• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor