Data Science Manager

Responsibilities:

• Lead and manage data science teams, overseeing the development and deployment of machine learning models and advanced analytics solutions.
• Define and execute data strategies aligned with business objectives, ensuring actionable insights drive decision-making.
• Collaborate with cross-functional teams, including engineering, product, and business stakeholders, to identify and solve complex data-related challenges.
• Ensure data integrity, governance, and security while optimizing data pipelines and infrastructure for scalability.
• Mentor and develop data scientists, providing technical guidance, performance feedback, and career development support.
• Stay updated on emerging trends, technologies, and best practices in data science and artificial intelligence (AI).
• Communicate findings effectively to both technical and non-technical stakeholders, translating insights into business impact.

Key Competencies:

• Strong problem-solving and analytical thinking skills to interpret complex data and drive insights.
• Leadership and people management abilities to guide and grow a high-performing data science team.
• Business acumen to align data science initiatives with organizational goals and drive measurable value.
• Effective communication skills for conveying technical concepts to diverse audiences.
• Decision-making capabilities based on data-driven approaches.

Technical Skills:

• Proficiency in programming languages such as Python, R, or SQL.
• Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn).
• Experience with big data technologies (Spark) and cloud platforms (AWS/ Azure/ GCP).
• Strong understanding of statistical modeling, predictive analytics, and deep learning.
• Experience with data visualization tools (Quicksight, Power BI, Matplotlib, Seaborn, Streamlit/Dash).
• GenAI: Experience with GenAI APIs, LLMs, Vectorization, Agentic AI and prompt engineering for domain-specific solutions
• MLOps: Ability to build reusable model pipelines and manage deployments using MLflow and Docker

Behavioural Competencies:

• Adaptability: Ability to pivot strategies based on evolving business needs and technological advancements.
• Learning Agility: Continuous learning mindset to keep up with emerging data science trends and methodologies.
• Teamwork: Collaborative approach to working with cross-functional teams, fostering knowledge sharing and innovation.

Certifications (Optional):

• Certified Data Scientist (CDS) – DASCA
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Coursera/edX Data Science Specializations (e.g., IBM, Stanford, Harvard)
• Data Engineering Certifications