Data Science Manager

As the Engineering Manager for Data Science, you will lead a high-performing team of Data Scientists, Machine Learning Engineers, and Data Engineers. You will sit at the intersection of advanced analytics and robust software engineering, guiding the team to design, build, deploy, and scale production-grade ML models and data pipelines.

The ideal candidate is a hands-on technical leader who has successfully transitioned from building algorithmic models to leading engineering teams. You will collaborate closely with Product Management, Core Engineering, and Business stakeholders to translate strategic visions into scalable, production-ready AI/ML products.

Key Responsibilities

1. People Leadership & Team Development

● Lead, mentor, and scale a cross-functional team of 5–10 Data Scientists and ML/Data Engineers. ● Foster a high-performance culture focused on innovation, technical excellence, and continuous learning.

● Conduct regular 1-on-1s, drive career development plans, and manage performance reviews.

● Participate actively in hiring to attract top-tier talent to the data organization.

2. Technical Leadership & Architecture

● Own the end-to-end lifecycle of ML models—from data ingestion and feature engineering to model training, deployment (CI/CD for ML), and real-time monitoring.

● Guide the architectural design of high-throughput data pipelines and robust ML infrastructure (MLOps).

● Enforce best practices for software engineering within the data science lifecycle, including code reviews, version control, testing frameworks, and documentation.

● Ensure the team balances rapid experimentation (R&D) with the delivery of stable, secure, and production-ready code.

3. Strategy & Delivery

● Partner with Product Managers and Business leaders to define the team's roadmap, prioritize tasks, and manage sprint deliveries.

● Translate ambiguous business problems into well-defined technical specifications and technical deliverables.

● Manage risks, unblock engineers, and manage technical debt aggressively to ensure predictable delivery timelines.

Requirements & Qualifications

Must-Haves:

● Experience: 7+ years of total experience in Data Science, Machine Learning, or Data Engineering, with at least 2–3 years in a formal leadership/management role.

● Strong Programming Skills: Proficient in Python or a compiled language like C/C++, alongside strong SQL skills.

● Core ML Knowledge: Deep understanding of Machine Learning fundamentals, Deep Learning, statistical modeling, and frameworks like TensorFlow, PyTorch, or Scikit-Learn.

● Data & MLOps Infrastructure: Hands-on experience with modern big data technologies (e.g., Spark, Hadoop, Kafka) and cloud platforms (AWS, GCP, or Azure). Familiarity with MLOps tools like MLflow, Kubeflow, or SageMaker is highly preferred.

● Agile Methodologies: Proven track record of managing engineering deliveries using Scrum/Agile frameworks.

● Education: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a highly quantitative field.

Nice-to-Haves:

● Experience implementing Generative AI, LLMs, or retrieval-augmented generation (RAG) architectures in production environments.

● Prior experience working in the [insert industry, e.g., Fintech, B2B SaaS, E-commerce] sector.

● Experience with graph databases or complex network relationship mapping.