AI Data Science Lead

2 days ago

Lucknow, Uttar Pradesh, India beBeeData Full time US$ 1,20,000 - US$ 1,60,000

AI Data Scientist Role

We seek an experienced AI data scientist to spearhead the development of innovative machine learning solutions.

Key Responsibilities:

  • Develop and implement predictive models, recommendation systems, and generative AI applications tailored to customer needs.
  • Serve as primary technical contact for customers, building trusted relationships with stakeholders from technical teams to C-level executives.
  • Lead proof-of-concepts, pilot deployments, and production implementations while ensuring seamless integration with customer systems.
  • Conduct data assessments, provide ML strategy recommendations, and guide customers through model lifecycle management.
  • Mentor customer teams and build internal data science capabilities through training and best practices sharing.
  • Gather customer feedback to enhance existing solutions and identify new product opportunities.

Required Qualifications:

  • Master's in Data Science, Computer Science, Statistics, or a related quantitative field.
  • 5+ years hands-on data science experience with 2+ years in customer-facing or consulting roles.
  • Proven track record delivering ML solutions in production environments.

Technical Skills:

  • Programming: Advanced Python/R, strong SQL capabilities.
  • ML Expertise: Deep learning, NLP, traditional ML algorithms, and generative AI/LLMs.
  • Frameworks: TensorFlow, PyTorch, scikit-learn, cloud ML services (AWS/Azure/GCP).
  • Tools: MLOps platforms, data visualization tools, big data technologies.

Business Skills:

  • Exceptional communication and presentation abilities for technical and non-technical audiences.
  • Strong project management and stakeholder relationship skills.
  • Customer-centric problem-solving mindset.
  • Ability to translate business requirements into technical solutions.

Success Metrics:

  • Customer satisfaction and retention rates.
  • Successful ML solution deployments from concept to production.
  • Revenue growth within assigned accounts.
  • Effective knowledge transfer and capability building.

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