Data Scientist (Machine Learning Focus) at iRecharge Tech-Innovations

• 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