iRecharge Tech-Innovations is an internet-powered distribution platform that enables users to purchase virtual products and services such as airtime and mobile data, internet subscriptions, pay-TV, and Bulk SMS.
- The ideal candidate will combine strong analytical expertise with fintech domain knowledge to optimize performance, detect patterns, and drive business growth.
Key Responsibilities
- Develop, test, and deploy machine learning models for use cases such as customer segmentation, fraud detection, transaction prediction, and user behavior analysis.
- Perform exploratory data analysis (EDA), data cleaning, and feature engineering on high-volume transaction datasets.
- Collaborate with product, engineering, and business teams to identify opportunities that improve customer experience and revenue.
- Design and automate dashboards and reports to track KPIs such as transaction success rates, customer retention, and revenue performance.
- Translate complex data insights into clear, actionable recommendations for stakeholders and leadership.
- Monitor model performance and retrain models to maintain accuracy and reliability.
- Work with large datasets from multiple sources including APIs, payment systems, databases, and cloud platforms.
- Support fraud analytics, anomaly detection, and risk modeling initiatives.
- Establish and promote data science best practices, including documentation and model governance.
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- 3–5 years of experience in data science, preferably within fintech, banking, or digital payments.
- Strong proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch).
- Experience with SQL and working knowledge of NoSQL databases.
- Solid understanding of machine learning algorithms and statistical analysis.
- Experience handling large-scale transaction or financial datasets.
- Proficiency in data visualization tools (Power BI, Tableau, or similar).
- Familiarity with cloud platforms (AWS, Azure, or Google Cloud).
- Strong analytical and problem-solving skills.
- Excellent communication skills with the ability to present insights to non-technical stakeholders.
Preferred Qualifications
- Experience in fraud detection, credit risk modeling, or financial analytics.
- Knowledge of MLOps and model deployment pipelines.
- Experience in a fast-paced fintech or startup environment.
Key Performance Indicators (KPIs)
- Model accuracy and performance
- Fraud detection efficiency and reduction rates
- Improvement in customer retention and transaction success rates
- Timeliness and business impact of insights delivered
- Adoption and usability of dashboards across teams
Key Competencies
- Data-Driven Thinking
- Business & Financial Acumen
- Collaboration & Stakeholder Management
- Innovation & Continuous Learning
- Attention to Detail
Method of Application
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