Associate Tech Lead - Data Science

Responsibilities

• Execute and own end‑to‑end development of AI/ML systems, from data ingestion and feature engineering to model deployment and monitoring.
• Provide inputs for the architectural direction of machine learning platforms, ensuring scalability, security, performance, and alignment with organizational standards.
• Lead the evaluation, selection, and integration of AI technologies, frameworks, and tools, including GenAI solutions.
• Collaborate with product, and domain teams to translate business requirements into well‑designed ML architectures and solution roadmaps.
• Oversee ML model lifecycle management, including experimentation, versioning, CI/CD integration, and continuous improvement.
• Mentor and guide data scientists, ML engineers, and analysts, fostering technical excellence and knowledge sharing.
• Conduct reviews of ML pipelines, code, and solution designs to ensure quality, maintainability, and adherence to best practices.
• Drive the implementation of MLOps practices such as automated model training, deployment, monitoring, and retraining workflows.
• Partner with cross-functional teams to troubleshoot complex ML and data pipeline issues and develop long‑term solutions.
• Stay current with emerging trends in AI/ML and GenAI, advising on opportunities to adopt new techniques and accelerate innovation.
• Ensure data quality, governance, compliance, and responsible AI principles are integrated into all AI/ML initiatives.
• Promote continuous improvement and help establish best practices in AI architecture, experimentation, model evaluation, and documentation.

Technical Skills:

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, or a related technical field; master’s degree in data science is preferable

• 8–10 years of experience in AI/ML development, advanced analytics, or data‑driven software engineering.

• Deep expertise in machine learning algorithms, statistical modeling, neural networks, NLP, or computer vision.

• Strong proficiency in Python and experience with AI/ML libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Keras etc.

• Hands‑on experience designing and deploying ML models into production environments at scale.

• Strong understanding of GenAI techniques, including LLMs, embeddings, vector databases, and prompt‑engineering concepts.

• Proficiency with SQL and experience working with large, complex datasets.

• Experience with cloud-based AI/ML platforms such as AWS SageMaker, Azure ML, or Google Vertex AI.

• Familiarity with MLOps tools and practices including CI/CD, model registries, feature stores, and monitoring frameworks.

• Strong understanding of distributed computing, microservices, APIs, and data engineering concepts.

• Knowledge of ML security, governance, and responsible AI guidelines is a plus.

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Soft Skills:

• Demonstrated ability in mentoring and guiding the technical teams.
• Excellent communication and storytelling skills for conveying complex AI concepts to technical and non‑technical audiences.
• Strong stakeholder management and the ability to collaborate effectively across functions.
• Strategic thinker with the ability to define long‑term AI roadmaps while driving short‑term execution.
• Strong problem‑solving and decision‑making capabilities, especially in ambiguous or fast‑changing environments.
• Highly organized with the ability to manage multiple projects and deliverables simultaneously.
• Proactive, innovative mindset with a strong focus on quality, reliability, ethics, and continuous improvement.

We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.