Data Scientist

Responsibilities

• Develop, evaluate, and deploy predictive and generative models for real production use cases
• Perform feature engineering and data preparation for modeling workflows
• Translate business and product questions into analytical solutions
• Build and maintain LLM powered features and services
• Develop retrieval augmented generation (RAG) pipelines using embeddings and vector databases
• Integrate LLMs with APIs and internal tools using structured function calling
• Finetune foundation models with parameter efficient approaches (e.g., LoRA)
• Evaluate model quality, detect hallucinations, and implement safety guardrails
• Use synthetic data to improve model performance, testing, and fairness
• Optimize inference performance and cost across different model providers
• Deploy and operate machine learning and GenAI models in production
• Build CI/CD pipelines for models and data workflows
• Monitor performance, data quality, and model drift
• Design versioning, rollback, and retraining strategies
• Partner with platform and infrastructure teams to ensure reliability and scalability
• Build low latency data pipelines and real time decisioning systems
• Work with streaming data and event driven architectures
• Support systems with strict uptime and response time requirements
• Contribute to feature stores used for both real time and batch inference

Qualifications

• 3–6 years of experience in data science, machine learning, or applied AI
• A degree (Master's or better preferred) in Computer Science, Data Science or related fields
• Strong Python and SQL skills
• Handson experience deploying models into production
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Experience with modern ML frameworks (PyTorch, TensorFlow, scikitlearn)
• Exposure to LLMs, embeddings, and GenAI workflows
• Ability to communicate clearly with engineers, product managers, and nontechnical partners

Nice to Have

• Experience with streaming data systems
• Experience operating models at scale
• Knowledge of MLOps tools and observability practices
• Experience building AI solutions that support customer facing products