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 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
Top Skills
What We Do
At Nissan Motor Corporation, we are transforming how people live and drive — and we need passionate innovators to help us create a more sustainable future. As a global automotive leader, we take pride in enriching the lives of our employees and believe your passion, talent, and dedication are essential to our success. That’s why we provide career growth opportunities to help you move ahead, internal mobility so you can explore other career paths, and a healthy work-life balance so you always feel your best. In the spirit of “doing what others don’t dare to do,” we value your knowledge and life experience. We work to ensure all employees have a voice and always feel comfortable using it. Join us on our journey into a future of possibility, and experience a career as exhilarating as the open road.
Why Work With Us
Nissan is a global company driven by the accomplishments of our employees. We find pride in our continued commitment to diversity and an exciting lineup of vehicles. Simply put, we are at the intersection of people, transportation, and the environment which keeps us at the forefront of innovation.
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