Apoorva S.

Associate Consultant

Agra, Uttar Pradesh, India4 yrs 2 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expertise in Data Science and Machine Learning.
Experience in developing recommendation systems.
Strong background in traffic modeling for smart cities.

Stackforce AI infers this person is a Data Science professional with a focus on Fintech and Smart City solutions.

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Skills

Core Skills

Data ScienceMachine Learning

Other Skills

C (Programming Language)C++Dashboard CreationData AnalysisFinancial ModelingGraph neural networkKerasLaTeXMATLABMicrosoft ExcelMulti-agent reinforcement learningPandas (Software)ProgrammingPythonR

About

Hard-working and dedicated, having experience in Data Science & Deep Learning. Interested in integrating Data Science into Finance and exploring the possibilities.

Experience

Anonymous

Quantitative Researcher(Automated Trading Architecture)

May 2022 – Present · 3 yrs 10 mos · Gurugram, Haryana, India · On-site

High frequency trading

Quantitative Analyst

Nov 2021 – Mar 2022 · 4 mos · Anonymus

T0uch

Data Science Intern

Aug 2021 – Oct 2021 · 2 mos · Work from home

Constructing a Recommendation model with the help of users Linkedin profile and their Social Graphs Proposing a graph neural network GraphRec, which can model data in social recommendations coherently Providing a principle approach to jointly capture interactions and opinions in the user-item graph Introducing a method to consider heterogeneous strengths of social relations mathematically

Recommendation modelGraph neural networkSocial recommendationsData ScienceMachine Learning

Government of andhra pradesh

Data Science Intern

Jul 2020 – Sep 2020 · 2 mos · Andhra Pradesh, India

Exploratory and Descriptively Analysed the unstructured data containing villagers queries and requirements
Worked on creating the Customer Experience Dashboard of GSWS wing of Government of Andhra Pradesh

Quantela inc.

Data Science Intern

Jun 2020 – Aug 2020 · 2 mos · Hyderabad, Telangana, India

Worked on efficient and low cost applications of regional traffic control and coordinated system models
Used models based on multi-agent reinforcement learning like Q-learning, a self-operating system
Formed a self adaptive system to auto-analyse speed for V2I with SCOOT, SCAT & fuzzy logic models
Did real-time route optimization in traffic modelling for smart cities by combining ACO & BCO models

Traffic control applicationsMulti-agent reinforcement learningReal-time route optimizationData Science