Key Responsibilities:
- 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.