Sanjoli P. Profile

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$30 USD / orë

london,

united kingdom

$30 USD / orë

Aktualisht është 5:04 p.d. këtu

U bashkua janar 2, 2019

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Sanjoli P.

@teamfruits

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$30 USD / orë

london,

united kingdom

$30 USD / orë

Asnjë

Punët e Plotësuara

Asnjë

Brenda Buxhetit

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Në Kohë

Asnjë

Shpeshtësia e Rimarrjes në Punë

Data Scientist | AI | Program Management

I possess expertise in quantitative data science and data analytics, with over 5 years of experience in these fields, working with fortune 500 corporations. Whether you need assistance in making data-driven business decisions, translating well-defined problems into data modeling, conducting data extraction and mining, or constructing machine learning-based data models, I am well-equipped to help. Additionally, I have skills in program management, enabling me to strategically plan, operationalize, and execute problem statements using a data-backed approach across various business segments - Knows python, SQL and other programming language for data modeling, data extraction and mining - Knows visualization tools such as Tableau - Full project management from start to end

Shikoni të gjitha Data Processing Executives në United Kingdom

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Vlerësimet

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Përvoja

Amazon

sht 2022 - E tashme

Managing projects end to end which includes strategical planing, operationalization, and execution using a data-backed approach across various business segments

Goldman Sachs

jan 2017 - gush 2021 (4 years, 7 months)

Engaged in data and analytic focused projects spanning life-cycle of trade. Responsible for managing overall project scope, delivery and developing analytic solutions to problem statements. While doing so, I developed the skills to perform data mining, extraction, summary, and modelling. Alongside, the results were put in detail using visualization tool such as Tableau

Arsimi

MBA

University of Oxford - Said Business School,

United Kingdom

2021 - 2022

(1 year)

B-Tech + M-Tech

National Institute of Technology Rourkela,

India

2011 - 2016

(5 years)

Botime

Reduction in execution cost of k-nearest neighbor based outlier detection method

Springer

Detecting outliers is crucial for uncovering vital information across diverse application domains. While k-nearest neighbor outlier detection methods have been effective, their prolonged execution time is a drawback. I've introduced a method to expedite k-nearest neighbor algorithms by promptly identifying normal data points. This eliminates the need to compute outlier scores for these points in subsequent processing, leading to significant time savings.