Technologist, Data Engineering

Senior Data Scientist

Technologist level

Key Responsibilities

• Lead the end-to-end data science lifecycle: problem definition, data acquisition, model development, deployment, and monitoring.
• Architect and implement scalable AI/ML solutions using modern frameworks, cloud platforms, and MLOps best practices.
• Drive GenAI initiatives including fine-tuning, prompt engineering, and integration into enterprise applications.
• Provide strategic direction and thought leadership on advanced analytics adoption across the business.
• Mentor, coach, and upskill a team of data scientists and engineers; foster a culture of innovation and collaboration.
• Partner with cross-functional teams (engineering, product, factory operations, IT) to translate business needs into data-driven solutions.
• Ensure model robustness, fairness, interpretability, and compliance with ethical AI standards.
• Design and oversee experimentation frameworks (A/B testing, causal inference, statistical modeling) for data-driven decision making.
• Stay ahead of emerging trends in AI, ML, and big data technologies; evaluate their potential for business impact.
• Present insights, models, and strategies to senior leadership and non-technical stakeholders in clear, actionable terms.
  

Qualifications

• MS/ME/MTech/PhD in Data Science, Statistics, Computer Science, or related fields.
• ~15 years of experience in data science, AI/ML, or advanced analytics, including leadership in complex projects.
• Proven expertise in:
• Machine Learning, Deep Learning, and Statistical Modeling
• Optimization techniques for solving complex, high-dimensional problems.
• GenAI applications including architectures like RAG, fine-tuning, and LLMOps.
• Synthetic data generation and handling highly imbalanced and high-volume datasets.
• GenAI applications including architectures like RAG, fine-tuning, and LLMOps.
• Experience with anomaly detection, pretrained transformers, and custom embedding models.
• Strong proficiency in Python and SQL for data wrangling, analysis, and modeling.
• Hands-on experience with TensorFlow, PyTorch, Pyspark, and related AI/ML frameworks.
• Deep understanding of Big Data platforms (e.g., Spark, Hadoop, distributed databases, cloud data warehouses).
• Experience in MLOps: model deployment, monitoring, versioning, and lifecycle management.
• Strong knowledge of data architecture, pipelines, and feature engineering at scale.
• Familiarity with data visualization tools: Tableau, Power BI, Matplotlib, Plotly.
• Excellent communication and stakeholder management skills, with the ability to influence at senior levels.
  

Additional Information