Description :

- 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.