SAYANDEEP GHOSH DASTIDAR

Results-driven data scientist with a strong foundation in statistics, machine learning and data analysis. Seeking to leverage expertise in data analysis and predictive modeling to extract actionable insights and drive strategic decision-making. Proactive manager with demonstrated leadership abilities, strategic planning expertise and problem-solving acumen. Assists senior managers with accomplishing demanding targets by encouraging staff and coordinating resources. Methodical and well-organized in optimizing coverage to meet operational demands.

• Hands-on experience with Linear Regression, Logistic Regression, Decision Trees, Random Forest, applied through various projects.
• Practical expertise in unsupervised learning techniques, including K-means clustering and hierarchical clustering.
• Proficient in time series analysis utilizing methods such as Holt-Winters, ARIMA and ARMA.
• Skilled in data visualization in R using ggplot2 and Plotly for creating interactive graphs.
• Experienced in feature engineering with R, including handling missing values and outliers, transforming variables, creating new features, and reshaping data with packages like dplyr and tidyr in R.

• Real Estate, A real estate agency wanted to reduce the negotiation time and improve closure for buyers and sellers of homes by ensuring that both sides were advised well on the potential sale/purchase price of the home. To that end the agency wanted to predict a transaction price for all the houses in its market which would be as close as possible to a price where the transaction would take place. Data regarding all possible variables and qualities of over 10,000 houses sold in the past was collected and analyzed., Created a predictive model using linear regression to arrive at a potential transaction price for all future transactions. Over the next 6 months, negotiation time was brought down from 3 weeks on average to 1 week. Deal closure went up by 25%

• Retail, A retail giant needs to plan its new store openings over the next 12-18 months and has multiple locations where the stores can be opened. It wants to understand which locations would be the best to open new stores in terms of market size and potential revenues. Relevant data regarding locations, population, sales and revenues of key products for stores opened in the past was provided to be analyzed and for model building., Created Random Forest and Logistic regression models to predict if a store should be opened or not in that particular location. Random Forest worked out to be a better model and 20 locations were selected to open new stores based on this analysis.

• ICICI BANK, Exceeded the stretch target at ICICI Bank in fiscal year 2022
• ICICI BANK, Resolved product issue through consumer testing
• ICICI BANK, Issued LC worth 2cr in 1 financial year
• IMI KOLKATA, 1st Position in Model United Nation

• Advance Excel- INTERNSHALA
• Data Science Specialist - IIT KANPUR
• Tableau - Edvancer
• R language & SQL - Edvancer