Data Science & Engineering Lead

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Science & Engineering Lead based in India.

This role sits at the intersection of advanced data engineering and applied machine learning, driving end-to-end AI and data innovation across complex, large-scale systems. You will lead the design, development, and deployment of machine learning models and data platforms that generate actionable business insights and production-ready intelligence. The position combines hands-on technical execution with architectural ownership, spanning ML model development, data pipelines, and cloud-native infrastructure. You will work with modern AI frameworks, distributed systems, and big data technologies to build scalable, high-performance solutions. The role also involves mentoring engineers and shaping best practices across data science, MLOps, and data engineering functions. This is a high-impact leadership opportunity in a fast-evolving, innovation-driven environment focused on real-world AI applications.

• Lead the design and implementation of supervised and unsupervised machine learning models, including regression, classification, ensemble methods, and advanced neural network architectures.
• Drive development of deep learning systems such as CNNs, RNNs, GANs, Transformers, and other state-of-the-art AI models for real-world applications.
• Oversee NLP and computer vision initiatives using modern frameworks and libraries to solve complex data problems.
• Architect and maintain scalable data pipelines for batch and streaming data using ETL tools and orchestration frameworks.
• Design and optimize lakehouse and data warehouse architectures, including Delta/Iceberg and bronze-silver-gold data models.
• Lead development of robust ETL workflows using tools such as Airflow, DBT, and Airbyte.
• Build and optimize cloud-native data and AI infrastructure on AWS or Azure, including services for compute, storage, and streaming.
• Oversee MLOps pipelines to ensure scalable, reliable, and automated deployment of machine learning models.
• Develop and maintain OLTP and OLAP data models across relational and NoSQL databases.
• Guide data visualization and BI initiatives using tools such as Power BI, Tableau, or QuickSight.
• Mentor and support junior engineers, fostering best practices in AI, ML, and data engineering.

Requirements:

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field; Master’s or PhD preferred.
• 7+ years of experience in data science, machine learning, or data engineering roles.
• Strong hands-on expertise in supervised and unsupervised learning, deep learning, and neural network architectures.
• Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
• Experience working with LLM tools and frameworks such as LangChain, Hugging Face, or OpenAI APIs.
• Strong background in big data processing using Spark and related distributed computing frameworks.
• Proven experience designing and deploying scalable data pipelines and ETL workflows.
• Deep understanding of cloud platforms (AWS or Azure), including services such as Lambda, Kinesis, Kafka, IAM, and networking.
• Hands-on experience with MLOps platforms such as SageMaker, Databricks, or Azure ML Studio.
• Strong knowledge of lakehouse architectures, data governance, and data quality frameworks.
• Experience with relational and NoSQL databases for both OLTP and OLAP systems.
• Strong leadership and mentoring abilities with excellent communication and cross-functional collaboration skills.
• Experience with Infrastructure as Code tools such as Terraform or CloudFormation is highly desirable.

Benefits:

• Opportunity to lead end-to-end AI and data initiatives in a high-growth, innovation-driven environment.
• Remote-friendly work model based in India.
• Exposure to cutting-edge machine learning, GenAI, and large-scale data engineering systems.
• Strong leadership responsibility with ownership of architecture and technical direction.
• Collaborative and engineering-focused culture emphasizing innovation and continuous learning.
• Access to modern cloud platforms and advanced AI/ML tooling ecosystems.
• Competitive compensation aligned with experience and leadership level.