Data Scientist – AI/ML

About the Role

Key Responsibilities:

• Design, develop, and deploy machine learning models and AI solutions for business problems across multiple domains.
• Collaborate with cross-functional teams including engineering, product, and analytics to identify opportunities for data-driven
  solutions.
• Perform exploratory data analysis (EDA), feature engineering, and model selection using industry best practices.
• Conduct experiments and perform model validation and performance tuning.
• Build reusable code and libraries for future use and contribute to data science platform improvements.
• Present findings and insights clearly to both technical and non-technical stakeholders.

Required Skills & Qualifications:

• 4+ years of relevant experience in data science, with hands-on experience in machine learning, deep learning, or AI systems.
• Proficiency in Python and relevant libraries (e.g., Pandas, Scikit-learn, TensorFlow, PyTorch, NumPy, etc.).
• Strong understanding of supervised, unsupervised, and reinforcement learning techniques.
• Experience with SQL, data wrangling, and working with large datasets.
• Exposure to cloud platforms like AWS, Azure, or GCP is a plus.
• Familiarity with MLOps, model deployment, and version control (e.g., Git).

Preferred Qualifications:

• Experience working with NLP, computer vision, or time-series forecasting.
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Power BI, Tableau).
• Understanding of A/B testing, causal inference, and statistical hypothesis testing.
• Ability to work in a fast-paced, agile environment with minimal supervision.
• Experience in GenAI frameworks (OpenAI, Vertex AI), multi-agent architectures, and MLOps tooling.

Tech Stack:

• Python, Java, SQL, JavaScript/TypeScript
• TensorFlow, PyTorch, Scikit-learn

Cloud Platform:

• GCP – Vertex AI, BigQuery, Cloud Functions, Cloud Run, Dataflow, Pub/Sub

Data & MLOps:

• BigQuery, AlloyDB, MongoDB, Redis
• Any relation Database – Oracle , MySQL, MSSQL
• Any of the MLOps – MLflow, Kubeflow, Airflow
• Docker, Kubernetes

GenAI & Agents:

• Vertex AI Gemini, OpenAI, Claude
• LangChain, LlamaIndex, FAISS, Pinecone
• RAG pipelines, autonomous agents, multi-agent orchestration, memory modules, tool use, prompt chaining

Dev Tools:

• GitHub Copilot, ChatGPT, Cursor AI or any other Gen AI Tools