Senior Machine Learning Strategist

2 days ago

Amrāvati, Maharashtra, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 30,00,000
Data Science and AI Leadership

As a senior Data Science and AI professional, you will lead our efforts in developing innovative machine learning solutions that drive business value.

  • You will collaborate with clients to understand their business objectives and challenges, providing data-driven insights and recommendations for AI/ML solution implementation.
  • Translate complex business needs into well-defined machine learning problem statements and solution workflows.
  • Solve business problems using analytics and machine learning techniques, including exploratory data analysis, feature engineering, and model development.
  • Develop and deploy ML models, focusing on supervised and unsupervised learning algorithms and model performance optimization.
  • Design scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, TensorFlow, and PyTorch.
  • Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP.
  • Communicate complex technical concepts effectively to stakeholders at various stages of engagement.
  • Stay current with developments in AI, ML modelling, and data engineering best practices, integrating them into project work.
Required Skills and Qualifications
  • 4 to 7 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment.
  • Strong exposure to banking, payments, fintech or Wealth/Asset management domains, with experience working on problems related to marketing analytics, customer churn and retention, credit risk assessment, fraud detection, and customer segmentation.
  • Bachelor's or Master's degree from a Tier 1 technical institute or MBA from Tier 1 institute.
  • Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, TensorFlow, PyTorch.
  • Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring.
  • Eagerness to learn and familiarity with developments in Agentic AI space.
  • Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting.
  • Effective communication and presentation skills for internal and client-facing interactions.
  • Ability to bridge technical solutions with business impact and drive value through data science initiatives.

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