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.

Sidharth Kriplani

Loyalytics AI

Specialist data engineer 

Data Engineer

Deloitte

DXC Technology

• Working in analytics domain for a US based client, assisting in evaluating the impact on sales through data analysis and studying the growth trends.
• Helped BI team in extracting insights from pool of sales data using ADF, SQL and python by using ETL activities and helped in developing 
  periodic reports to capture sales trends performance and promotional activities for portfolio of items based on these trends.
• Created a scheduler job using ADF (corresponding to CSV files) to fully automate ETL activities which helped reducing the manual effort almost by 95%.

4 years of experience in data engineering.
Working on Azure cloud services, ADF, Databricks, SQL, ETL/ELT, PySpark, streaming,Hive & Hadoop Ecosystem.

#hive #hadoop #pyspark #ADF #ADLS #Databricks

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Sidharth Kriplani

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