AVP_Data Science

Role & responsibilities

AI & Machine Learning

• Design, develop, and implement machine learning models for predictive analytics, natural language processing (NLP), computer vision, or recommendation systems.
• Lead end-to-end AI project lifecycles, including validation, productionization, and optimization using frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Stay updated with the latest advancements in Generative AI and foundation models, evaluating and integrating them as needed.
• Partner with cross-functional teams to identify AI use cases and deliver measurable business value.

Data Science & Analytics

• Lead the end-to-end analytics lifecycle, from data exploration to actionable insights and product development.
• Collaborate with data engineers and analysts to build impactful solutions that drive business outcomes.
• Leadership & Strategy
• Mentor and guide junior team members, fostering their professional growth.

Cloud Architecture & Engineering

• Contribute to the enterprise AI and cloud strategy, including roadmap planning and vision setting. Architect and design cloud-native AI and data platforms on GCP (preferred), AWS, or Azure.
• Optimize data pipelines and machine learning workflows using services like Vertex AI, SageMaker, or Azure ML.
• Ensure scalable, secure, and cost-efficient architectures with robust CI/CD pipelines in cloud environments.

Preferred candidate profile

• Proficiency in Python and SQL.
• Strong expertise in machine learning frameworks like TensorFlow, PyTorch, Hugging Face, and scikit learn
• Hands-on expertise with large language models (LLMs), vector databases, and retrieval-augmented generation (RAG) techniques
• In-depth knowledge of cloud platforms, preferably GCP, but also AWS or Azure.
• Familiarity with data engineering tools such as BigQuery, Kafka, Snowflake.
• Sound knowledge of data privacy, governance, and ethical AI practices.
• Expertise in MLOps practices, including CI/CD pipelines, model monitoring, and A/B testing.