Sidharth Kriplani

Business Analyst

Bengaluru, Karnataka, India3 yrs 7 mos experience

Key Highlights

  • Key contributor to feature store establishment at McAfee.
  • Expert in transforming raw data into actionable insights.
  • Proficient in SQL, Python, and data engineering methodologies.
Stackforce AI infers this person is a Data Scientist specializing in SaaS and Data Engineering.

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Skills

Core Skills

Data ScienceFeature EngineeringData EngineeringData Analysis

Other Skills

Azure DatabricksSQLDatabricksPythonPySparkPandas (Software)Python (Programming Language)Statistical Data AnalysisSQL Server Integration Services (SSIS)Apache AirflowEDAA/B TestingHypothesis TestingKPI DashboardsAmazon Web Services (AWS)

About

I am a seasoned Data Scientist/Analyst with a rich experience spanning two years in the field. Over the past year, I have proudly contributed my skills and expertise to the dynamic teams of both Data Science (Personalisation - ML) and Data Engineering at McAfee. My journey has been marked by a relentless pursuit of excellence and a passion for transforming data into meaningful, concrete insights. My proficiency in SQL, Python, Spark, my ability to figure out a way around blockers on the go, and my decisiveness with sound reasoning has been the cornerstone of my success, enabling me to extract, analyze, and interpret data with precision. My adeptness in these tools has empowered me to spearhead feature engineering projects, playing a pivotal role in enhancing the value of data and optimizing its use for strategic decision-making. One of my significant accomplishments includes being a key contributor to the establishment of a feature store at McAfee. As we all know, from start to end, 70% of time in most Machine Learning projects is spent on finding out correct POCs within the company, communicating with them to acquiring the correct data and also understanding it, predicting probable metrics to be obtained from it, and then performing the pre-processing. Developing a feature store involved a lot of Feature Engineering, gaining close familiarity with the raw data, understanding the business and performing a lot of SQL intricacies, not to forget a lot of internal communication. The objective was to cut down on the preprocessing time and provide Data Scientists with data they could rapidly build prototypes upon without having to worry about the entire logistics of how it came to be. The nature of this 'pre-processed' data was that it could support a lot of Data Science efforts, since this data captured the essential behavior of our customers. This innovative solution has not only streamlined workflows but has also facilitated the acceleration of data-driven innovation within the organization. Motivated by my unwavering commitment to continuous growth and a thirst for knowledge, I am eager to expand my horizons into the realms of Data Engineering and Data Science. Drawing upon my foundation in analytics and my proficiency in key technologies, I am excited to take on new challenges and leverage my skill set to drive transformative insights and solutions. In essence, my journey reflects a fusion of experience, technical prowess, and an unrelenting desire to create value from data.

Experience

3 yrs 7 mos
Total Experience
11 mos
Average Tenure
--
Current Experience

Impact analytics

Senior Business Analyst

Jul 2025Dec 2025 · 5 mos · Karnataka, India · Hybrid

Uber

Senior Data Analyst

Feb 2025Jul 2025 · 5 mos · Bengaluru · Hybrid

Mcafee

2 roles

Data Scientist

Nov 2023Dec 2024 · 1 yr 1 mo · Bengaluru, Karnataka, India

Azure DatabricksFeature EngineeringData Science

Data Analyst

Sep 2022Nov 2023 · 1 yr 2 mos · Bengaluru, Karnataka, India

  • Summary:
  • Supported the Data Science team in building an end-to-end feature store by processing raw data into consumable features for Machine Learning models. This effort aimed at gauging each customer's propensity to engage with our product offerings.
  • Objective:
  • Construct a comprehensive 360-degree customer profile by leveraging available data to formulate both quantitative and qualitative features that depict customer interactions across various communication channels.
  • Process:
  • 1. Data Understanding: Engaged in thorough analysis of vast datasets with 50+ features and billions of rows, liaising with multiple stakeholders and team leads to ensure accuracy and relevance.
  • 2. Feature Engineering: Crafted SQL scripts and automated the process using PySpark to transform raw data into ML-ready features.
  • 3. Feature Store Maintenance: Scheduled weekly jobs to update feature tables, ensuring the latest data is always ready for rapid prototyping and informed decision-making.
  • Additional Project:
  • Psychographic Persona Development: Drove a project to process raw HTML bodies of popup alerts to create data that could be ingested by ML models, aiming to build psychographic personas of users to boost our personalization initiatives.
  • Stakeholder Engagement:
  • Worked closely with the Marketing and E-commerce teams to align the data analysis and feature engineering processes with business objectives.
  • Notes:
  • Communication channels analyzed included email and popup alerts.
  • This initiative was a precursor to the broader MLOps architecture development.
  • The data handled was primarily tabular in nature.
  • Ensured the maintenance of feature tables since the Feature Store project was started during McAfee's ongoing migration between cloud providers, thus ensuring project continuity.
  • Tech Stack: SQL, Databricks, Python, PySpark
Azure DatabricksData AnalysisData Engineering

Capgemini

Senior Analyst

Feb 2022Sep 2022 · 7 mos · India

  • Data Engineering:
  • 1. Processed SAS scripts to PySpark to help a client migrate their database management system using GPT3 testing
Pandas (Software)Python (Programming Language)Data Engineering

Education

Liverpool John Moores University

Master's degree — Artificial Intelligence

Nov 2021Jun 2022

International Institute of Information Technology Bangalore

PGD — Machine Learning and Artificial Intelligence

Jan 2020Jan 2021

Maharaja Agrasen Institute Of Technology, Delhi

B.Tech — Electrical and Electronics Engineering

Jan 2014Jan 2018

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