ML Engineer for Healthcare and Enterprise

AKANKSHA AYINDLA

ML ENGINEER

Michigan, USA +1-630-***-**** *****************@*****.*** linkedin.com/in/akanksha-ayindla github.com/akanksha-ayindla

PROFESSIONAL SUMMARY

ML Engineer with experience building, training, and evaluating machine learning models across healthcare and enterprise environments. Proficient in Python, SQL, Scikit-learn, and TensorFlow, with hands-on experience across the full model development lifecycle: data preprocessing, feature engineering, model selection, training, evaluation, and hyperparameter tuning for regression, classification, and deep learning (CNN) use cases. Skilled in building the ETL pipelines and SQL-based data infrastructure that feed model training and evaluation, and in supporting AWS-based (S3, Lambda) workflows around model development. Strong domain depth in healthcare claims analytics and enterprise KPI reporting, with a track record of translating ambiguous business problems into trained, evaluated ML models. Effective collaborator across actuarial, compliance, engineering, and operations stakeholders.

TECHNICAL SKILLS

Languages

Python, SQL, Java, R

Machine Learning

Scikit-learn, XGBoost, Random Forest, Logistic Regression, Decision Trees, SVM, KNN, Feature Engineering, Model Selection, Model Evaluation, Hyperparameter Tuning, Cross-Validation

Deep Learning

TensorFlow, Keras, CNN, Transfer Learning, VGG16, Neural Networks, Image Classification, Model Training

Data Engineering

ETL Pipeline Design & Automation, Data Preprocessing, Data Cleaning, Data Validation, SQL Query Optimization, Data Warehousing

Statistics & Analytics

Statistical Analysis, Hypothesis Testing, Exploratory Data Analysis (EDA), Forecasting, Regression, Classification, Predictive Modeling

Cloud & Infrastructure

AWS (S3, Lambda), Cloud-Based Analytics Workflows

Databases

MySQL, PostgreSQL, Data Warehousing

Visualization / BI

Power BI, Excel, Matplotlib, Seaborn, KPI Dashboarding

Tools & Practices

Git, GitHub, Jupyter Notebook, VS Code, Agile Collaboration, Cross-Functional Delivery

Domain

Healthcare Analytics, Claims Data, Enterprise Reporting

PROFESSIONAL EXPERIENCE

ML Engineer Jun 2024 – Present

Molina Healthcare, USA

●Built and evaluated machine learning models in Python to analyze healthcare claims, provider, and membership data and identify cost drivers.

●Applied regression and statistical techniques (hypothesis testing) to model cost drivers and utilization patterns across large healthcare datasets.

●Performed data preprocessing, cleaning, feature engineering, and validation to prepare high-quality training data for model development.

●Built the SQL-based data pipelines against enterprise data warehouses that feed model training and evaluation workflows.

●Built and maintained Power BI dashboards to surface model outputs and KPIs for executive-level decision-making.

●Conducted trend analysis and forecasting using statistical and ML methods to support healthcare operations planning.

●Collaborated cross-functionally with actuarial, compliance, and operations teams to define modeling requirements and validate outputs.

●Supported cloud-based model development workflows using AWS S3 and Lambda for data storage and lightweight processing.

Associate ML Engineer Feb 2022 – Dec 2023

GlobalLogic Pvt. Ltd., Hyderabad, India

●Built predictive machine learning models in Python using Scikit-learn to analyze large-scale healthcare datasets and generate actionable insights.

●Performed feature engineering, data transformation, and model selection to improve machine learning model performance and generalization.

●Developed and evaluated regression and classification models for forecasting and predictive analysis use cases.

●Designed automated ETL pipelines using SQL, reducing manual reporting effort by 35%, to supply clean training data for model development.

●Created Power BI dashboards to monitor model performance and business KPIs.

●Conducted statistical analysis and hypothesis testing to validate model assumptions before deployment into reporting workflows.

●Partnered with engineering teams to support cloud-based model training and evaluation workflows in AWS environments.

●Performed root cause analysis on model and pipeline inefficiencies and recommended process improvements.

ML Engineer (Entry-Level) Nov 2021 – Jan 2022

DeepEdge Technologies

●Assisted in building and training machine learning models for classification and regression tasks under senior data scientist guidance.

●Performed data cleaning, preprocessing, and exploratory data analysis (EDA) to prepare training datasets.

●Trained and evaluated models using Scikit-learn, including basic hyperparameter adjustment.

●Built data visualizations in Matplotlib and Seaborn to identify patterns and inform model design decisions.

●Gained foundational exposure to the full ML model lifecycle, from data ingestion through evaluation.

SELECTED MACHINE LEARNING PROJECTS

Healthcare Claims Cost Prediction System

Technologies: Python, Scikit-learn, Pandas, NumPy

●Built predictive regression models on large healthcare claims datasets to forecast cost trends.

●Applied feature engineering and model tuning to improve forecasting accuracy.

●Delivered outputs used to inform healthcare cost and utilization planning.

Customer Churn Prediction Model

Technologies: Python, Scikit-learn, Logistic Regression, Random Forest

●Developed classification models using Logistic Regression and Random Forest to predict customer churn.

●Applied cross-validation and hyperparameter tuning to strengthen model performance.

●Identified key features driving customer retention and behavior to inform business strategy.

COVID-19 Detection Using Deep Learning

Technologies: TensorFlow, Keras, CNN, VGG16

●Built CNN and VGG16-based image classification models for medical image analysis.

●Applied image preprocessing and data augmentation to improve model robustness.

●Achieved strong classification accuracy on medical imaging data.

Sales & Revenue BI Dashboard

Technologies: Power BI, SQL

●Designed interactive Power BI dashboards for business performance tracking.

●Automated ETL workflows, reducing reporting time by 40%.

●Built KPI dashboards used to support executive decision-making.

CORE COMPETENCIES

Machine Learning Model Development • Model Training & Evaluation • Deep Learning (CNN) • Feature Engineering • Hyperparameter Tuning • Predictive Analytics • Statistical Analysis • Data Preprocessing • ETL Pipeline Design • SQL Optimization • AWS Cloud Workflows • Healthcare Analytics • KPI Dashboarding • Cross-Functional Collaboration

EDUCATION

Master of Science in Data Analytics 2024 – 2025

Indiana Wesleyan University, USA

Bachelor of Technology (B.Tech) 2019 – 2023

Jawaharlal Nehru Technological University (JNTU), India

CERTIFICATIONS

●AWS Academy Machine Learning

●Introduction to Data Analytics — Coursera

●Python for Data Science — Udemy

ADDITIONAL INFORMATION

●Experience spans the full ML lifecycle: data preprocessing, feature engineering, model development, training, evaluation, and tuning.

●Strong domain depth in healthcare analytics, claims data, and enterprise reporting environments.

●Skilled at translating ambiguous business problems into structured, data-driven ML solutions.

●Experienced working in cross-functional, agile team environments across the U.S. and India.

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