Senior Data Scientist

We are seeking a highly skilled and passionate Data Scientist to design, build, and productionize enterprise-grade analytics and GenAI-powered solutions that enhance insights, recommendations, and decision-making across enterprise platforms. The role focuses on developing, deploying, and operating machine learning and applied GenAI models—including LLM-based insight generation, summarization, and decision augmentation—using large-scale structured and semi-structured data, with strong emphasis on scalability, reliability, governance, and enterprise readiness.

Responsibilities:

Advanced Analytics & Data Science:

• Translate business problems into data science, statistical, and machine learning solutions that drive measurable outcomes across enterprise use cases.
• Perform data exploration, feature engineering, model development, and evaluation on large-scale structured and semi-structured datasets.
• Build and deploy predictive, prescriptive, and descriptive models, ensuring interpretability, robustness, and alignment with business objectives.
• Partner closely with business, product, and analytics teams to validate assumptions, define success metrics, and deliver actionable insights.

Applied GenAI & LLM Enablement:

• Apply GenAI techniques to augment data science workflows, including LLM-based insight generation, summarization, classification, and decision support.
• Design and implement Retrieval-Augmented Generation (RAG) solutions to ground LLM outputs in enterprise data and analytical results.
• Collaborate on GenAI-enabled analytical applications (e.g., conversational analytics, insight assistants) with a focus on accuracy, relevance, and explainability rather than pure agent orchestration.
• Evaluate and benchmark GenAI outputs using quantitative and qualitative metrics, ensuring alignment with business and analytical standards.

Enterprise Productionization & MLOps / LLMOps:

• Productionize data science and GenAI models using enterprise-grade MLOps / LLMOps practices, including versioning, deployment, monitoring, and retraining strategies.
• Build scalable, secure, and reliable analytical pipelines in collaboration with Data Engineering and Cloud teams.
• Monitor model performance, data drift, and GenAI output quality, and drive continuous improvements based on real-world usage.
• Ensure solutions meet enterprise requirements for governance, security, compliance, and responsible AI.

Performance Measurement & Continuous Improvement:

• Define and track model and GenAI performance metrics (accuracy, stability, bias, latency, business impact).
• Run experiments and controlled rollouts to optimize models, GenAI prompts, and retrieval strategies.
• Continuously enhance solutions through feedback loops, experimentation, and evolving business needs.

Skills:

• Strong foundation in statistics, machine learning, and applied data science, including feature engineering, model evaluation, and performance tuning.
• Experience building predictive, descriptive, and prescriptive models on large-scale structured and semi-structured data.
• Proficiency in Python, SQL, and Spark, with hands-on experience in data processing and analytical pipelines.
• Hands-on experience with the Databricks ecosystem (Databricks SQL, MLflow, Feature Store, and Jobs) to build, deploy, and monitor data science and GenAI solutions at enterprise scale.
• Experience with ML frameworks such as PyTorch and/or TensorFlow for model development and experimentation.
• Hands-on experience using LangChain and LangGraph to operationalize LLM-based analytical workflows, including RAG and prompt design, and evaluation techniques, with focus on analytical and decision-support use cases.
• Practical exposure to MLOps / LLMOps practices, including model and prompt versioning, deployment, monitoring, and retraining.
• Experience tracking model quality, drift, and GenAI output reliability in production.
• Strong understanding of data quality, explainability, responsible AI, and enterprise governance requirements.

Qualifications and Experience:

8+ years of experience in Data Science / AI Engineering, including

• 6+ years building and deploying machine learning models (supervised, unsupervised, and time-series), covering feature engineering, model evaluation, and performance optimization.
• 4+ years working with NLP or language-based systems, including text classification, information extraction, and semantic modeling.
• 2+ years delivering GenAI or conversational AI solutions in production, with focus on applied LLM use cases, RAG, and enterprise deployment.