Data Science Manager

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