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 solution
- sMLOps: Ability to build reusable model pipelines and manage deployments using MLflow and Docke
rBehavioural 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) DASC
- AAWS Certified Machine Learning Specialt
- yMicrosoft Certified: Azure AI Engineer Associat
- eCoursera/edX Data Science Specializations (e.g., IBM, Stanford, Harvard)
- Data Engineering Certification
