Sahil Batra

SKILLS

Cloud: GCP, AWS

Database Programming: Relational Databases (IBM DB2, SQL Server, Teradata), BigQuery, RedShift

ML Tools: SageMaker, Vertex, Transformer, MCP, Langchain, TFX, Kubeflow

Programming Language: Python, Bash, SQL

ETL Tools: Informatica 9.1, DataProc(Spark), DataFlow(Beam)

Software/Tools: Airflow, Git (CI/CD), JupyterLab, VS Code, Docker, Kubernetes, GCP, Power BI, Comet

Packages: scikit-learn, numpy, pandas, sklearn, tensorflow, pytorch, xgboost ray

Certifications: Data Engineering Nanodegree (Udacity), Deep Learning Specialization (Coursera), Natural Language Processing Specialization (Coursera), Splunk Fundamentals & Advanced Searching (Splunk), GIAC Security Essentials (GSEC)

EXPERIENCE

2022 - Present

Sr. ML Engineer

Remote, Canada

Built end to end ML model for fraud onboarding use cases—including point-of-payment, card cashing, and no-intent-to-ship fraud—covering feature engineering, model training, real-time inference and adoption of fraud scores across products. Prec 90%; Savings of $5M+.
Led ML Ops productionalization for fraud detection models, enhancing model evaluation, tracking, and real-time deployment. 100+ manual hours saved, allowed fast iteration of models within hours.
Helped build transformer-decoder networks using Sequence data to identify fraud at different points of merchant journey. Led to identification of Bot related incidents.

2018 - 2022

Sr. Data Scientist

Remote, Canada

Manage the AI platform & strategy, stakeholder engagement and technical work across 4 data scientists & data engineers.
Built AI systems & Data Pipelines
Created infection risk KPI and provided time to return based on LSTM model. The model was shared across the Security Executive Council and 500 companies across the globe.
Classical imbalanced fraud risk scoring model to detect fraudulent emails with model metrics on live data - Prec 90%, $5M + saved, end to end system to support Fraud ops.
Identified characteristics of employees susceptible to phishing based on phishing simulations data (Prec 83% & Rec 89%).
Applied an AutoEncoder to reconstruct daily network data to identify anomalous traffic using Producer Consumer Ratio, Session & Volume of traffic across network enabling reduction in false positive rate by 60%.

2020 - 2022

Mentor

Canada

Teaching & providing industry perspective for MIT & UT Austin - Artificial Intelligence & Machine Learning courses with overall rating of 4.8/5.

2016 - 2018

Data Scientist, BI & Analytics Lead

USA / Canada

Built Metrics & KPI from ground up for Servers Business Unit on weekly operational performance (100+ attendees).
Applied ML models in sales domain:
Customer market segmentation using K Prototype based on company attributes, and recency, frequency & monetary attributes for sales targeting, resulting in a 28% increase in qualified leads.
Commodity data clustering to identify trends of most common vs least configurations, reducing sample inventory holding costs by 20%.

2015 - 2016

Data Scientist, Analytics

USA

Created ML models:
Clustering using KNN & Quantile Regression to identify customer software spend across different market segments, revealing 2 high-potential market segments for targeting.
Lead generation cross-sell model to help sales to identify better candidates for cold calling, improving cross-sell conversion rate from 9% to 37%.

2013 - 2014

Software Developer, Business Analytics

India

Utilized informatica to build data pipelines & hadoop for data storage for ingestion of employee workday data.

2010 - 2013

Software Engineer, BI

Noida, India / United Kingdom

Built ETL Data pipelines using SAS & Informatica to pull data feeds to create data warehouse & data marts in SQL & IBM DB2 using Fact/Dims.

PROJECTS

SKILLS

EXPERIENCE

EDUCATION

WORK AUTHORIZATION