Senior Data Solutions Architect

3 hours ago

Gandhinagar, Gujarat, India beBeeMachineLearning Full time US$ 1,50,000 - US$ 2,00,000

Unlock Your Career Potential as a Senior Data Scientist

Job Overview

This is an exceptional opportunity to join our organization as a Senior Data Scientist, working closely with enterprise customers and leading the development of innovative machine learning solutions.

Key Responsibilities:
  • Client Engagement: Foster strong relationships with customers through regular communication and collaboration.
  • Solution Design: Architect end-to-end ML solutions that meet customer needs and drive business growth.
  • Implementation Leadership: Oversee the deployment of ML solutions, ensuring seamless integration with customer systems.
  • Technical Consulting: Provide expert guidance on data assessments, ML strategy, and model lifecycle management.
  • Knowledge Transfer: Share best practices and expertise with customer teams and internal stakeholders.
  • Product Development: Collaborate with customers to enhance existing solutions and identify new product opportunities.
Required Qualifications:Education & Experience
  • Master's in Data Science, Computer Science, Statistics, or a related quantitative field.
  • 5+ years of hands-on data science experience, with 2+ years in customer-facing or consulting roles.
  • Proven track record of delivering ML solutions in production environments.
Technical Skills
  • Programming: Advanced skills in Python and R, with 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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