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.
