Data Science and ML 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