- Trivandrum
- 3 - 0 Years
- Not disclosed
- Senior Magento developer
- 1 Days ago
Strong expertise in the .NET ecosystem: C#, ASP.NET Core, LINQ, REST APIs Modern architecture principles (DDD, Clean Architecture, Microservices) SQL knowledge Cloud & DevOps: Azure (Functions, App Services, Key Vault, SQL/Cosmos) Strong knowledge of version control systems like GitHub. CI/CD pipelines (Jenkins) Containerization (Docker; Kubernetes experience is a plus) AI Skills & Work Style Used to using AI tools to improve productivity. Knowledge of Generative & Agentic AI tools. Demonstrates strong cross-functional engineering capability in development, architecture, DevOps, and QA activities. Identifies and avoids over-engineering or unnecessary complexity Professional Attitude Team player, Take Ownership & Continuous learner
Role Overview We are looking for an experienced Data Scientist with strong analytical capabilities and hands-on experience in building, deploying, and maintaining machine learning models in a FinTech environment. The role requires translating business problems into data-driven solutions, particularly in areas such as customer propensity modelling, risk analytics, and customer behaviour analysis. The candidate should be comfortable working with large datasets, collaborating with business teams, and operationalizing models in production environments. Key Responsibilities 1. Data Analysis & Business Insights Analyse large structured and semi-structured datasets to generate business insights for financial products and customer behaviour. Translate business problems into analytical frameworks and data science solutions. Perform exploratory data analysis to identify trends, patterns, and opportunities for product growth. 2. Machine Learning Model Development Design, develop, and validate machine learning models for use cases such as: Customer propensity models Cross-sell / up-sell prediction Customer segmentation Risk or fraud-related analytics Apply statistical and machine learning techniques such as logistic regression, tree-based models, boosting algorithms, and clustering. 3. Model Deployment & Lifecycle Management Deploy ML models into production environments. Build pipelines for model monitoring, retraining, and performance tracking. Maintain and optimize existing models to ensure accuracy and stability. 4. Collaboration with Business & Product Teams Work closely with product, risk, marketing, and business teams to understand requirements. Convert analytical outputs into actionable recommendations. Support decision-making through data-driven insights and dashboards. 5. Advanced Analytics & AI (Good to Have) Knowledge or hands-on exposure to Large Language Models (LLMs) and Generative AI. Experience in LLM-powered analytics assistants, RAG pipelines, or conversational data interfaces is an advantage.
Role Overview We are looking for a Data Analyst with strong experience in the FinTech domain to drive data-driven decision-making across lending, payments, or financial services. The role requires deep expertise in SQL and data visualization, with the ability to translate complex datasets into actionable insights for business stakeholders. Key Responsibilities Extract, transform, and analyse large datasets using SQL from multiple data sources (data lakes, warehouses, CRM systems). Develop and maintain dashboards and reports using visualization tools (e.g., Power BI, Tableau, Amazon Qucksight). Perform cohort analysis, funnel analysis, and customer segmentation for financial products (loans, insurance, payments). Analyse key metrics such as disbursement, conversion rates, churn, delinquency, and portfolio performance. Collaborate with business teams (risk, product, marketing, collections) to identify growth and optimization opportunities. Build automated reporting pipelines to reduce manual effort and improve data reliability. Ensure data quality, consistency, and governance across reporting systems. Support A/B testing and experiment analysis for product and marketing initiatives. Mandatory Skills Strong proficiency in SQL (joins, window functions, CTEs, query optimization). Hands-on experience in Data Visualization tools (Power BI / Tableau / Looker). Experience working with large-scale structured datasets. Strong analytical thinking and problem-solving skills. Ability to communicate insights clearly to both technical and non-technical stakeholders.