Rajeev is passionate about data and machine learning and has more than five years of experience in data science projects across numerous industries and applications. He's currently focused on cutting-edge technologies such as TensorFlow, Keras, deep learning, and most of the Python data science stack. Rajeev has used these skills to solve many real business problems in NLP, image processing, and time series domains.
Worked with a US-based food aggregator startup on data engineering and scraping, using the Python data science stack, Jupyter Notebook, and AWS services.
Handled the recommendation engine for the user, a food and restaurant recommendation.
Developed the scraping application using Python and deployed it using AWS services.
Managed the business intelligence team, acting as a senior data scientist for the client.
Worked as a quant researcher, using advanced forms of quantitative techniques and artificial intelligence to generate investment recommendations across multiple asset classes, including stocks, ETFs, options, and cryptocurrencies.
Created a dashboard for the growth and marketing and leadership teams using Dash, Plotly, and Tableau.
Worked with the Land Transport Authority, Singapore to implement the vision to convert the city into a digital and intelligent one to improve the efficiency of services for the citizens, using machine learning, predictive modeling, and data mining.
Built a recommendation system for an eCommerce site; it recommended the best possible items to buy based on customer history and collaborative filtering.
Helped with customer churn prediction by developing a classification algorithm for a retail bank to identify customers likely to churn balances in the next quarter by at least 50% vis-a-vis current quarter.
Created a classification algorithm for a retail bank to improve sales from existing customers by cross-selling one of its product, the personal loan (customer cross-sales).
Set up business benefits of around £43 million over five years in customer retention, cost savings, and new business opportunities at an estimated cost of around £12 million.
Acted as a vital member of the steering committee that identified user needs and developed customized solutions for around 250,000 Barclaycard acquiring merchants.
Led a project team of 147 members including solution architects, designers, developers, and testers spread across multi-geographical locations through the entire project development life cycle.
Consistently stayed within around 5% of resource and budget forecast monthly.
Recognized as problem solver within a team of 22 project managers in the portfolio of annual spend over £70 million.
Worked for IBM US to optimize its US facility leases to run its operation.
Developed a Python model to improve facility utilization, reduce facility operations cost and reduce lease cost along with number of business constraints.
Worked closely with the C-level executive and product management team to analyze the survey and produced data/reports.
Helped the product team and executive team to make more informed decisions—increasing market share through the identification of new opportunity, target segments and devising ingenious new ways of resolving constraints.
Newristics is a US-based global leader applying decision-heuristic science to marketing.
It automates message scoring by comparing new messages against old ones and analyzing their adherence to heuristic psychology.
The project utilizes XGBoost and deep neural network seq-to-seq learning models, incorporating NLP features, word embeddings, graph analysis, and TF-IDF similarity.
