Job Description

Purpose of the Job

We are seeking a hands-on Data Scientist with strong foundations in statistics and machine learning, experience in cloud-based environments, and exposure to modern AI/GenAI practices. You will build, deploy, and scale production-grade ML and AI solutions in collaboration with business, data engineering, and MLOps teams.

Job Description

• Design, develop, and deploy ML, AI, and GenAI solutions for business problems
• Own the end-to-end data science lifecycle: problem definition to deployment and monitoring
• Build and optimize ML/DL, NLP, LLM, and GenAI models (including RAG and prompt engineering)
• Develop models using Python-based ML frameworks and deploy them at scale
• Leverage cloud platforms (GCP) for model training, inference, and data processing
• Implement MLOps best practices (CI/CD, model versioning, monitoring, retraining)
• Collaborate with data engineers on pipelines, feature stores, and integrations
• Communicate insights and model outcomes to technical and non-technical stakeholders
• Mentor junior data scientists and contribute to best practices

Job Requirements - Experience and Education

• Strong proficiency in Python, SQL, and ML libraries (NumPy, Pandas, Scikit-learn)
• Solid understanding of statistics, probability, and ML algorithms
• Hands-on experience with TensorFlow, PyTorch, XGBoost, LightGBM
• Experience with NLP / GenAI / LLMs (embeddings, transformers)
• Cloud experience on GCP (e.g., Vertex AI)
• Experience with Docker, APIs (FastAPI/Flask), and MLOps tools (MLflow, Airflow, CI/CD)
• Bachelor’s or Master’s degree in a relevant field
• Experience with production ML systems and real-world AI deployments
• Exposure to responsible AI, explainability, and bias mitigation
• Prior mentoring or technical leadership experience

Leadership Behaviors

Building Outstanding Teams

Setting a clear direction

Simplification

Collaborate & break silos

Execution & Accountability

Growth mindset

Innovation

Inclusion

External focus

Skills