Lead and drive the development of advanced machine learning models to solve high-impact business challenges.
Collaborate with stakeholders (business leaders, product managers, engineering teams) to identify opportunities where data science can add business value.
Embed yourself with business to understand the strategy and propose data solutions to accelerate outcomes.
Design and implement scalable data pipelines and real-time analytics solutions.
Guide and mentor data scientists, fostering a culture of innovation and best practices.
Conduct deep-dive analyses to extract meaningful insights from complex and large datasets.
Ensure machine learning models are explainable, reproducible, and aligned with business objectives.
Present findings and recommendations to key stakeholders in a clear and actionable manner.
Work closely with engineering teams to deploy ML models into production environments at scale.
Own end-to-end model lifecycle management, including data preprocessing, feature engineering, model training, validation, and monitoring.
Required Qualifications:
Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (Ph.D. preferred but not mandatory).
8+ years of experience in data science, machine learning, or applied statistics. Experience in financial services, fintech, e-commerce, or similar data-driven industries.
Strong expertise in Python, SQL with proficiency in machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
Solid understanding of big data technologies (Spark, PySpark, Hadoop, or similar).
Hands-on experience in model deployment using cloud platforms (AWS, GCP, Azure) and ML Ops frameworks (MLflow, Kubeflow, etc.).
Expertise in statistical modeling, predictive analytics.
Experience working with real-time and batch-processing systems.
Demonstrated ability to translate business problems into data science solutions.
Excellent problem-solving, critical-thinking, and analytical skills.
Strong communication and stakeholder management skills, with the ability to explain technical concepts to non-technical audiences.
Preferred Qualifications:
Experience in financial services, fintech, e-commerce, or similar data-driven industries.
Soft Skills:
Mentorship: Ability to mentor data scientists and foster a culture of knowledge-sharing.
Effective Communication: Strong verbal and written communication skills to engage with business stakeholders.
Strategic Thinking: Ability to align data science initiatives with business objectives.
Adaptability: Comfortable working in a fast-paced and evolving business environment.