Head of Data Science

4 weeks ago

india Datamatics Full time

ROLE: Head of Data Science & Analytics

Reporting Manager: President

Overview:

  • He/ She will be leading the global delivery team to build a highly scalable and extensible Analytics/ Data Science/ Data Engineering team that provides the foundation for collecting, storing, modelling, and analysing massive data sets from multiple channels
  • The ideal candidate will have demonstrated experience in delivery management, project planning, resource planning, delivery quality, maintain margins and customer satisfaction
  • This is a Data analytics & engineering leadership role for a strong leader that loves to operate in trenches with the team

Key Responsibilities:

  • A leadership position in the Advanced Analytics and Data Sciences practice
  • Responsible for business growth (order booking) and establishing company as a thought leader in the Analytics space
  • Build offerings, platforms, solutions and capabilities - to create differentiating position for Company in the market
  • Build solutions and capabilities -focusing on leading edge technologies such as AI/ML- collaborating with Company labs.
  • Establish and nurture strategic relationships with partners and vendors
  • Engage with customers as a thought leader by building and leading an elite pool of architects/specialists/data scientists
  • Work with other practices to drive Analytics led transformation of offerings as well as the way we deliver our services and value propositions.
  • Manage Presales, Solutioning, Sales Enablement responsibilities for the practice
  • As a people manager, build and nurture a talented pool of specialists
  • Establish connects with industry stakeholders and manage analyst relationships


Desired Skills & Behaviours:


  • High preference for a PhD in one of the following fields from a premier institute:
  • Physics, Electrical Engineering, Mathematics, Computer Science, Statistics
  • Overall experience of 20+ years. 15+ years of relevant experience in Data Science /Data Analytics Domain
  • Experience with implementing data governance models and processes in a large, highly complex business environment for at least 5 years
  • In-depth experience of data/information management practices, systems and processes including information lifecycle management, data modelling, master data management, meta-data management
  • Cross-industry experience highly preferred. 4+ years of international experience working with Global teams
  • 3-5 years of experience as an IT/Technology Head/Leader
  • Prior experience in handling portfolio of Enterprise Data Management , Data Science Domain with Latest AI Trends
  • Fluent in structured and unstructured data, its management, and modern data transformation methodologies.
  • Demonstrated industry experience to:
  • Define and create complex models to generate valuable insights from real world data (large, sparse, and noisy datasets)
  • Deploy such models into production environments.
  • Have a deep and thorough understanding of the latest state-of the art AI technologies. Have a detailed understanding of all main network architectures, deployment modes, data augmentation and preparation, and performance analysis of model architectures
  • Excellent communication (written, verbal, and public speaking) as well as time management skills.
  • Ability to plan and execute operational and strategic activities.
  • Excellent interpersonal & communication skills, including scientific writing, presenting, and formal briefings.
  • Ability to evolve with a rapidly changing environment.
  • Maintains knowledge of current and emerging technologies.
  • Self-directed and responsive to customer expectations.
  • Strong analytical and logical problem solving skills.
  • Strong interpersonal communication and relational skills, good organizational and project management skills; sound judgment; initiative; flexibility; detail-oriented.

KPI of this Role:

  • Grow & Maintain Revenue
  • Team development
  • Improve Client CSAT
  • Maintain Margin
  • Define Value of Delivery

Accountability during a Project Lifecycle:

  • Fulfilment check during proposal phase
  • Margin health
  • Feasibility
  • Training & Interview prep
  • Offshore team onboarding

Day to day Delivery

  • Common delivery plan across Data Analytics/ Data Science/ Data Engineering/ Devops/ UI & iteration on delivery plan
  • Continuous customer expectation setting
  • Delivery quality
  • Timeliness of Delivery
  • Risk prediction
  • Team procurement & replacement
  • Renewal & Handoff.

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