Data Scientist (A)

Location: Bengaluru, India

Department: Projects & Delivery

Experience: 2-3 years

Skills: Classical ML, Data Science, LLM, Machine Learning

Data Scientist

Experience: 2-3 years | Location: Bengaluru (Hybrid)

Job Description: Focus on Classical Machine Learning (70%) and Generative AI (30%)

Key Responsibilities: 

Large-Scale Data Handling, PySpark, & Databricks Deployment 

• Efficiently handle and model billions of data points using multi-cluster data processing frameworks (PySpark, Spark SQL).
• Expertise on Databricks/AWS is a must have: Ability to design, write, scale, and monitor end-to-end ML Pipelines on Databricks/AWS.
• Proven expertise to run and manage Databricks data pipelines in real time for low-latency decision-making.
• Develop and implement scalable deployment pipelines using Docker and AWS services (ECR, Lambda, Step Functions).

Classical Machine Learning 

• Owning the entire workstreams end to end, from initial designs & POC by building custom machine learning solutions as needed till the business impact calculation of the use-case while ensuring modularity, scalability, and production-ready codebase.
• Design and implement custom models, loss functions and be able to handle nuanced conversations of trade offs between various modelling choices.
• Apply specialized modeling for marketing scenarios (Targeting, Budget optimisation, Churn) and data limitations (Sparse/incomplete labels, Single class learning).

Generative AI & Large Language Models :

• Practical experience in building LLM-ready Data Management layers for large-scale structured and unstructured data.
• Apply foundational understanding of LLM Agents and multi-agent systems (e.g., Agent-Critique, ReACT, Agent Collaboration), advanced prompting, LLM evaluation, confidence grading, and Human-in-the-Loop systems.

Must Have Technical Skills 

Data Pipelines, PySpark & Databricks 

• Proficiency in Python and its data science ecosystem (NumPy, Pandas, Dask, PySpark) for large-scale data processing.
• Expert, hands-on experience with Databricks for MLOps, pipeline orchestration, and real-time deployment.
• Ability to perform effective feature engineering by understanding complex business objectives.

Core Machine Learning & Deep Learning

• Classical ML : Tree Based Models, GLMs’, Clustering Models etc.
• Deep Learning : ANN, 1D/2D/3D Convolutional Neural Networks (ConvNets), LSTMs, Transformer models.
• Strong proficiency in PU learning, single-class learning, representation learning, alongside traditional ML approaches.
• Advanced understanding and application of model explainability techniques (e.g., SHAP, LIME).
• Hands-on experience with ML/DL libraries such as Scikit-learn, TensorFlow/Keras, and PyTorch.

Others

• Experience utilizing large-scale language models (GPT-4, Mistral, Llama, Claude) through prompt engineering and custom fine tuning.
• Code Versioning Systems : Github, Git

• Communication Skills : Of all the things, this is perhaps the most important soft skill for us, you must be able toCapture the attention of your audience - usually in client calls Succinctly put across your ideas to your team members Bring clarity of thought and next steps to the table and present it well.
• Presentation Skills : Be able to visually present your ideas on a white board Be able to build a compelling presentation for CxOs in a top down manner with an angle of business impact in mind.
• Problem Solving Skills : Be able to leverage various internal tools, client datasets to craft a problem in the shortest time possible. Be able to make trade-offs keeping the timelines in mind.

Good to Have

• Background in Pharma Domain.
• Knowledge of Recommender Systems & Next Best Action Systems.

Benefits and Perks