Job Description:
You are responsible for contributing to the development of AI/ML and advanced analytics solutions that will both drive the innovation of our product offering and also assist our day to day business decisions. This entails understanding of retail financial and consumer behavior data, identifying business use cases, implementing and measuring the effectiveness of various AI/ML models and analytical techniques in addressing business problems and identifying new opportunities.
Technology stack we use:
AWS, Python, Redshift, Airflow, Kafka, Spark, EKS, Metaflow, MLflow, FeastFS
Key Responsibilities:
- Data Analysis and Insights Generation: Analyze financial transactions and consumer behavior data to identify trends and actionable insights.
- ML Modelling: Develop and implement AI/ML solutions to drive product innovation and support business decision-making processes.
- Model Design and Evaluation: Design, develop, and evaluate the effectiveness of various AI/ML models and techniques to solve business challenges and optimize opportunities.
- Model Monitoring and Optimization: Monitor and refine AI/ML models and analytics solutions to ensure they remain effective and aligned with evolving business goals.
- Model Productization: Ensure the successful scaling and integration of machine learning models into production environments.
- Business Use Case Identification: Collaborate with cross-functional teams to understand business needs and translate them into use cases for AI/ML models and analytics.
- Stakeholder Communication: Communicate findings, insights, and recommendations clearly to both technical and non-technical stakeholders.
- Team Leadership and Management: Lead and mentor a small team of data scientists and machine learning engineers to foster collaboration and ensure the timely delivery of AI/ML solutions.
- Roadmap Development and Execution: Supporting Head of Data in developing and maintaining a strategic roadmap for AI/ML initiatives according to the business priorities, timelines, and resource allocation.
Requirements:
- Relevant degree in Mathematics and Statistics, Computer Science, or Engineering.
- At least 5+ years of working experience relevant to the field of data science.
- Practitioner of data-driven decision making
- Proficient in data visualization and data storytelling
- Proficiency in languages such as Python and SQL is essential. Experience with languages like Java may also be beneficial.
- Proficiency in performing statistical analysis on large datasets to extract meaningful and actionable insights.
- In-depth knowledge of machine learning algorithms, including supervised and unsupervised learning, deep learning, etc.
- Experience with cloud services for building scalable data solutions.
- Experience in MLOps is an added bonus
- Excellent communication and documentation skills.
