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Sangeetha Komallapalli

Senior Technical Lead/Data Scientist

  • KARNATAKA, KA
  • 10 years
Notice Period: 60 Days

    Candidate's About

    Experienced Data Scientist with 4+ years of expertise in developing data-driven solutions that enhance efficiency and accuracy of internal data processing. Proficient in catastrophe modeling with 8+ years of experience. Skilled in end-to-end project leadership using C, C++, and Python.

    Work & Experience

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    Software Engineer/Data Scientist Team Lead

    Aug 17 - PRESENT

    Technology

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      Python
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      C++
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      SQL
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      Java
    • Lead a team of software engineers and data scientists in developing and delivering high-quality software solutions that meet business requirements.
    • Define project objectives, scope, and timelines, and develop project plans and schedules to ensure timely and effective completion of projects.
    • Manage and mentor team members, provide technical guidance, and support their profes...
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    Melanoma Detection Assignment

    Software Engineer/Data Scientist Team Lead

    • The melanoma detection assignment involves building a CNN (convolutional neural network) based model that can accurately detect melanoma, a deadly form of skin cancer.
    • Melanoma accounts for 75% of skin cancer deaths, making early detection crucial for successful treatment and improved outcomes.
    • The model will use deep learning techniques to analyze medical images and identify signs of melanoma.
    • The project involved data collection, cleaning, and preprocessing, as well as model training, validation, and testing.
    • The model took into account various factors such as lesion size, shape, color, and texture, as well as patient demographics and medical history.
    • The ultimate goal was to create a highly accurate and reliable model that can be used to aid in the early detection and diagnosis of melanoma, potentially saving lives and improving patient outcomes.
    • Python
    • C++
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    Automatic Ticket Classification

    Software Engineer/Data Scientist Team Lead

    • The automatic ticket classification project involved creating a model that can classify customer complaints based on the products and services mentioned in the ticket.
    • The goal was to automate the ticket classification process, reducing the need for manual categorization and improving overall efficiency and accuracy.
    • The project involved data collection, cleaning, and preprocessing, as well as model selection, training, and evaluation.
    • Machine learning algorithms such as Naive Bayes or support vector machines were used to develop the classification model.
    • The model analyzed text data in the ticket to identify key phrases and keywords related to specific products and services.
    • Once the model has been trained and validated, it can be used to automatically classify incoming customer complaints, directing them to the appropriate department or team for resolution.
    • The ultimate goal was to improve the customer experience by ensuring that complaints are handled quickly and efficiently, leading to higher satisfaction and improved retention rates.
    • Python
    • C++
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    Senior Software Engineer

    Jun 13 - Jul 17

    Technology

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      C++
    • Designed and developed software applications, features, and enhancements that met business requirements and adhered to established coding standards and best practices.
    • Participated in all phases of the software development life cycle, including requirements gathering, design, development, testing, and deployment.
    • Worked collaboratively with cross-functional teams including ...
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    Lending Club

    Senior Software Engineer

    • Lending Club aims to detect patterns that suggest whether a borrower is likely to default on a loan.
    • These patterns can be used to take actions such as denying loans, reducing loan amounts, or lending to high-risk applicants at higher interest rates.
    • The goal was to improve the overall risk management of the lending process and minimize the risk of default.
    • By identifying risky borrowers, Lending Club can better allocate its resources and reduce the likelihood of losses.
    • The use of predictive analytics and machine learning can help to identify these patterns and inform lending decisions.
    • Python
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    Bike sharing assignment

    Senior Software Engineer

    • The bike-sharing assignment involved building a model to predict demand for shared bikes.
    • A bike-sharing system is a service that provides short-term access to bikes for a fee or for free.
    • The goal was to use multiple linear regression to develop a model that accurately predicts demand for shared bikes.
    • The model took into account various factors such as time of day, day of the week, weather conditions, and other variables that may impact demand.
    • Accurately predicting demand can help bike-sharing companies better manage their resources, optimize pricing, and improve customer satisfaction.
    • The development of the model involved data collection, cleaning, and analysis, as well as model selection and evaluation.
    • The ultimate goal was to create a reliable and accurate predictive model that can inform decision-making and improve the overall efficiency and effectiveness of bike-sharing systems.
    • Python
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    Telecom Churn

    Senior Software Engineer

    • The goal of the telecom churn project was to analyze customer-level data from a leading telecom company.
    • The objective was to build predictive models that can identify customers who are at high risk of churn (cancellation of service) and determine the main indicators of churn.
    • By identifying customers who are at risk of churn, the telecom company can take proactive measures to retain them and reduce overall churn rates.
    • The project involved data collection, cleaning, and analysis, as well as model selection and evaluation.
    • Machine learning algorithms and predictive analytics techniques will be used to develop the models and identify the most important predictors of churn.
    • The models took into account various factors such as customer demographics, usage patterns, and customer service interactions.
    • The ultimate goal was to create accurate and reliable models that can be used to inform customer retention strategies and improve the overall profitability and success of the telecom company.
    • Python
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    2017-09 - Current Lending Club Aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc Bike Sharing Assignment A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short te...
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    Education

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    Master of Science: Artificial Intelligence And Machine Learning

    Liverpool John Moores - UK
    2021 - 2023
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    Post Graduate Diploma: Artificial Intelligence And Machine Learning

    IIIT - Bengaluru, India
    2021 - 2022
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    Bachelor of Engineering: Computer Science

    Osmania University - Hyderabad, India
    2009 - 2013

    Certificates

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    Machine Learning Expert using Python

    Edvancer