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)
