Dinesh K.

Dinesh K.

Data science professional with 7 years of experience

New Delhi , India

Experience: 7 Years

Dinesh

New Delhi , India

Data science professional with 7 years of experience

46080 USD / Year

  • Immediate: Available

7 Years

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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

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Portfolio Projects

Inpatient admission forecasting

Company

Inpatient admission forecasting

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.

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Skills

Data Science

Tools

Data studio

Classification of high risk patients for dialysis using rare event logistic regression

Company

Classification of high risk patients for dialysis using rare event logistic regression

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.

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Tools

Data studio

CHA Clustering

Company

CHA Clustering

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.

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Skills

Data Science

Tools

Data studio

Fraud Management solution

Company

Fraud Management solution

Description

  • Fraud detection during import of goods
  • For example: An importer may 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.  

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Tools

Data studio