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JOBNOC

Delivery Head - Data Science - Retail/Healthcare/Lifesciences

JOBNOC

Gurgaon/Gurugram, IN; Hyderabad, IN; Pune, IN; Bangalore, INPosted 4 days ago
full time
Salary
Not disclosed
Posted: October 24, 2026
|
Source: external

Required Skills

Data Science
Delivery Head
Delivery Management
IT Project Management

About This Role

Job location: [{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Gurgaon/Gurugram","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Hyderabad","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Pune","addressCountry":"IN"}},{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bangalore","addressCountry":"IN"}}] Work arrangement: Employment type: FULL_TIME Salary: "" Experience requirements: {"monthsOfExperience":156,"educational":{"credentialCategory":"bachelor degree"}} Education requirements: "" Role Overview: We are seeking a dynamic and results-driven Director to lead Data Science (DS) delivery projects, combining deep expertise in data science methodologies with strong project management skills. The ideal candidate will have a strong track record of driving data-driven decision-making, managing end-to-end delivery of complex projects, and leading teams of data scientists and analysts in achieving business outcomes. Key Responsibilities: Data Science Leadership: - Drive end-to-end execution of data science projects, from conceptualization to deployment. - Apply advanced analytics, machine learning, and AI techniques to solve complex business problems. - Ensure high-quality and scalable solutions by applying best practices in model development and validation. Project Management: - Oversee project delivery timelines, budgets, and quality metrics. - Act as the bridge between business stakeholders and technical teams, translating business needs into technical requirements. - Identify and mitigate risks in project execution while maintaining alignment with strategic objectives. Team Leadership: - Lead and mentor a team of data scientists, data engineers, and analysts. - Foster a culture of innovation, collaboration, and continuous learning within the team. - Ensure the team is equipped with the right tools, techniques, and training to meet evolving business needs. Stakeholder Engagement: - Collaborate with senior leadership to identify opportunities where data science can create business value. - Communicate complex technical solutions and insights in a clear, actionable manner to both technical and non-technical audiences. - Build and maintain strong relationships with internal and external stakeholders. Strategic Vision: - Define and implement the data science strategy in alignment with organizational goals. - Stay updated with emerging trends and technologies in data science, ensuring their adoption within the team where applicable. - Drive thought leadership initiatives and contribute to organizational data maturity. Required Qualifications: - 13-21 years of overall experience, with at least 8-10 years in data science roles and 5+ years in project/program management. - Proven expertise in advanced analytics, machine learning, deep learning, and AI. - Hands-on experience with programming languages such as Python, R, or Scala, and frameworks like TensorFlow, PyTorch, or scikit-learn. - Strong experience in managing data science delivery projects across multiple domains. - Proficiency in project management methodologies (Agile, Scrum, or Waterfall) and tools like Jira, MS Project, or Asana. - Excellent communication, problem-solving, and stakeholder management skills. - Advanced degree in Computer Science, Data Science, Mathematics, Statistics, or a related field; MBA or PMP certification is a plus. - Experience in deploying data science solutions in cloud environments (AWS, Azure, GCP). Preferred Skills: - Familiarity with big data technologies like Hadoop, Spark, and Hive. - Experience with visualization tools like Tableau, Power BI, or similar. - Strong business acumen with the ability to align data initiatives to strategic objectives.

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