+1 (415) 644 5123
+44 1253 335595
+91 9875 492266
Dinesh
New Delhi
,
India
Data science professional with 7 years of experience
46080 USD / Year
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Immediate: Available
7 Years
About Me
- Business analytics professional with 6+ years of Experience in HR analytics, Retail, Taxation, Risk & Fraud Analytics and Clinical Analytics.
- Currently working as Manager- Analytics with Max Healthcare....
- Currently working as Manager- Analytics with Max Healthcare.
- Previously worked as an Associate Consultant with Kie Square Consulting on Fraud management project for CBEC - Ministry of Finance
- Expertise in converting business problem into a statistical problem and suggest solutions accordingly.
- Experience in leading and managing a team of analysts and associate analysts.
- Clinical Analytics: Carry out analytical project with the team and develop models for prediction of LOS, Bill amount, Patient safety etc.
- Fraud Analytics: Identification of Fraud Patterns, Anomaly detection, Fraud mitigation rules generation and forecast revenue.
- Retail Analytics: Collect historical data for Autumn Winter and Spring Summer collections and build a statistical recommendations model based on it.
- HR Analytics : Calculating Employee Engagement and create reports for different units and assist in Action Planning
- Statistical Techniques: Linear Regression, Logistic Regression, Forecasting, Decision Tree, Cluster Analysis, Factor Analysis.
- Tools: R , SAS ,SPSS ,Excel ,SAP Lumira
Skills
Portfolio Projects
Description
- Statistical classification of Custom house agents basis transactions and behaviors.(K-Means clustering)
- There are CHA agents authorised by Government to facilitate clearance of import of good for importers from the government.
- These CHAs sublicense their authority to further agents for better business.
- To create policies and to differentiate different types of CHAs \,w e created clusters so that the policy makes can create policies keeping in mind the different types of agents they would have to deal with.
Description
- Time series analysis model(ARIMA) to identify patient admission in various department and hospitals of the group to use it for strategic decision like resource planning and budgeting
- The in-patient admission planning was a pain point for the management and they had problems dealing with bed allocation across medical seasons and by departments
- On analysis we found that the annual revenue could be increased by 12-15% by better bed allocation and resource planning during different seasons.
- Further going with Time series analysis to solve this problem, we found that the series had both monthly and weekly seasonality.
- We took care of both the seasonality during the model development and piloted for one of the major hospitals.
- On implementation of model development ,we found the Mean absolute percentage error(MAPE) to be between 2.5-7% for different hospitals and departments.
Description
- Fraud detection during import of goods
- For example: An importermay declare gold as silver to evade duty on silver or may undervalue it.
- We used Decision tress to create rules that would classify transactions as fraudulent or not and assist inspectors on port in fraud detection.
Description
- Development of rare event logistic model to predict the effect of dyes used in contrast procedures on patients resulting in renal failure.
- Medically , it is known that a dye or contrast that is inserted in the patients is known to affect the kidney of 2-3% patients.
- But, we found that the event rate was much higher(6-7%) in our case.
- So,to improve patient care, we created a rare event logistic model using R wherein we assigned probability scores for each patient and classified high risk patients.
Verifications
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Profile Verified
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Preferred Language
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English - Conversational
Available Timezones
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