Financial Data Analyst

We are looking for a highly analytical and technically skilled Analyst to join our team. In this role, you will leverage statistical modelling, machine learning, and advanced analytics to solve complex financial problems across treasury, cash flow forecasting, macroeconomic analysis, and investment recommendations. You will work with large-scale financial and economic datasets to build predictive models, develop data-driven solutions, and collaborate with cross-functional teams to deliver intelligent financial products. Responsibilities Design, develop, and deploy predictive machine learning models for cash flow forecasting and treasury analytics. Build forecasting models using time-series techniques and statistical methods. Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing on structured and unstructured datasets. Develop data pipelines to ingest, clean, transform, and analyse financial and macroeconomic data from sources such as the RBI and other public datasets. Apply machine learning algorithms to identify patterns, anomalies, and financial risk indicators. Build recommendation models and predictive analytics solutions for investment products. Evaluate and optimise model performance using appropriate validation techniques and performance metrics. Collaborate with Product, Engineering, and Business teams to translate analytical insights into scalable product features. Create dashboards, reports, and visualisations to communicate insights to technical and business stakeholders. Continuously monitor, retrain, and improve models based on changing market conditions and new data. Predictive analytics for treasury and cash flow forecasting. Time-series forecasting and macroeconomic prediction models. Financial risk scoring and anomaly detection. Investment recommendation and ranking systems. NLP and alternative data analysis for financial insights (where applicable). AI-powered analytics solutions for retail wealth management products. Requirements Master's or bachelor's degree in computer science, data science, statistics, mathematics, economics or a related quantitative discipline. 3-6 years of experience in data science, machine learning, advanced analytics, or quantitative modelling. Strong programming skills in Python. Experience with time-series forecasting techniques such as ARIMA, Prophet, LSTM, or Transformer-based forecasting models. Strong understanding of statistics, probability, hypothesis testing, regression, classification, clustering, and optimisation techniques. Experience working with financial, transactional, or time-series datasets. Familiarity with Git and collaborative development practices. Strong problem-solving and analytical mindset. Passionate about applying machine learning to real-world business problems. Comfortable working with ambiguous datasets and building solutions from scratch. Able to communicate technical concepts effectively to non-technical stakeholders. Self-driven, curious, and eager to experiment with new algorithms and technologies. Preferred Qualifications Experience working with financial markets, treasury operations, banking, investment research, or fintech. Understanding of macroeconomic indicators, financial modelling, and quantitative finance. CFA (completed or pursuing) is an added advantage. This job was posted by Nidhi Khedekar from Kodo. We are looking for a highly analytical and technically skilled Analyst to join our team. In this role, you will leverage statistical modelling, machine learning, and advanced analytics to solve complex financial problems across treasury, cash flow forecasting, macroeconomic analysis, and investment recommendations. You will work with large-scale financial and economic datasets to build predictive models, develop data-driven solutions, and collaborate with cross-functional teams to deliver intelligent financial products. Responsibilities Design, develop, and deploy predictive machine learning models for cash flow forecasting and treasury analytics. Build forecasting models using time-series techniques and statistical methods. Perform exploratory data analysis (EDA), feature engineering, and hypothesis testing on structured and unstructured datasets. Develop data pipelines to ingest, clean, transform, and analyse financial and macroeconomic data from sources such as the RBI and other public datasets. Apply machine learning algorithms to identify patterns, anomalies, and financial risk indicators. Build recommendation models and predictive analytics solutions for investment products. Evaluate and optimise model performance using appropriate validation techniques and performance metrics. Collaborate with Product, Engineering, and Business teams to translate analytical insights into scalable product features. Create dashboards, reports, and visualisations to communicate insights to