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

KANINI

Associate - Data & Analytics

Junior Associate - Data & Analytics

Trainee Associate - IT Application Development

The Sparks Foundation

Data Science & Business Analytics

Generation Infinite Machines

1. Responsible for intensive research work, data   collection, and formation. 
2. Analyze the research findings, draw
inferences and conclusions in response to
the research query. 
3. Report the findings in the form of a
structured report in excel.

Data Research Analyst

Data Visualisation - Apache Superset, Power BI
Database - MySql, Postgres
Programming - Python
Data Science - ML, NLP
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I possess a comprehensive skill set in the field of data, with a strong foundation in business intelligence tools such as Apache Superset and Power BI. My proficiency extends to programming languages like Python and SQL, enabling me to navigate the intricacies of data science seamlessly. I have actively contributed to projects involving Natural Language Processing and machine learning, showcasing my ability to derive valuable insights from diverse datasets.

In the realm of business intelligence, I have demonstrated my expertise by effectively utilizing tools like Apache Superset and Power BI to transform raw data into meaningful visualizations and reports. This proficiency, coupled with my programming skills in Python and mastery of SQL, empowers me to approach data science challenges with a holistic perspective. My hands-on experience in Natural Language Processing and machine learning further underscores my commitment to staying at the forefront of technological advancements in the data field.

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Sidharth Kriplani

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