Data Science Manager, Genai – Sfl Scientific

Manager role at Deloitte's SFL Scientific practice, focused on leading teams to design, develop, and deploy novel AI/GenAI solutions for clients across various industries. Responsibilities include technical direction, project strategy, client engagement, model development, validation, and deployment, with a focus on delivering measurable business outcomes.

What you'd actually do

  1. Support identification of high-value AI opportunities that drive industry advantage, representing an organization's AI vision through strategic delivery and industry.
  2. Serve as the technical lead on projects to drive the technical strategy, roadmap, and prototyping of AI/ML solutions to meet each clients’ unique requirements
  3. Engage and guide a diverse set of clients with high autonomy in AI strategy and adoption, including understanding organizational needs, performing exploratory data analysis (EDA), building and validating models, and deploying models into production
  4. Lead comprehensive AI initiatives spanning predictive and generative AI, overseeing development of advanced models and ensuring systems are scalable, efficient, and adhere to requirements and AI guidelines
  5. Support an interdisciplinary team of data scientists, engineers, and solution architects to achieve technical delivery objectives and real-world performance for production and research applications

Skills

Required

  • AI/ML expertise
  • GenAI expertise
  • Computer vision
  • Natural language processing (NLP)
  • Time-series analysis
  • Graph neural networks
  • Client-facing consulting
  • Team leadership
  • Project management
  • Technical strategy development
  • Model validation
  • Model deployment

Nice to have

  • Experience in healthcare, life sciences, manufacturing, consumer, or energy industries
  • Experience with AI strategy and adoption
  • Experience with EDA
  • Experience with AI guidelines
  • Experience with production and research applications

What the JD emphasized

  • technical direction
  • client engagements
  • deploying models into production
  • AI strategy and adoption
  • building and validating models
  • deploying models into production
  • AI guidelines
  • production and research applications

Other signals

  • client engagements
  • deploying models into production
  • AI strategy and adoption
  • leading comprehensive AI initiatives