Citi Group Off Campus Drive 2024 Hiring for Summer Analyst

CITI Group Off Campus Drive 2024 Hiring for Summer Analyst

Citi Group Off Campus Drive 2024: Exciting opportunity for aspiring analysts! CITI Group is conducting an Off Campus Drive in 2024 for Summer Analysts specializing in Quantitative fields. As a global financial institution, CITI Group provides a dynamic environment where analysts can gain hands-on experience and develop essential skills in quantitative analysis. In this role, Summer Analysts will work on real-world projects, collaborate with experienced professionals, and contribute to innovative solutions. Join CITI Group’s drive and kickstart your career in finance with a company committed to excellence in quantitative analysis.

Company Name: Citi Group

Website: Citi

Wikipedia: Citi Wiki

Job Profile: Summer Analyst (Quant)

Degree Needed: B.E/B.Tech/M.E/M.Tech

Batch Eligible: 2023/2024

Work Location: Mumbai, India

Salary: Best In Industry

Experience Level: 0 – 2 Years

Job Description of Citi Group Off Campus Drive 2024:

Citi is looking to have summer interns for its team of highly talented individuals who are working on the research and development of analytics, signals and trading tools used for Citi’s Equity business. The work covers a number of product lines including pre-trade, intra-trade, post-trade products, electronic execution and central risk platforms across all major global markets.

Education for Citi Group Off Campus Drive 2024:

• Pursing Master’s/Bachelor’s degree in Computer Science/Electronics & Communications/Electrical Engineering/Mathematics/Statistics/Mathematics & Computing from a premier academic institution.
• Students from other departments with projects or relevant courses in Data science, machine learning, statistics, C++/python programming can also apply for the position.
• Minimum CGPA: 7.5

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Skills Required for Citi Group Off Campus Drive 2024:

• Strong analytical and quantitative background, with good knowledge in one or more of the following disciplines – probability, quantitative finance, computer science, linear algebra, numerical methods, statistics, econometrics, time series analysis
• Strong programming and computer science skills
• Good knowledge of Python, C++ or Java (any one)
• Knowledge of Q/KDB+ is preferable or any No-SQL databases
• Understanding of web tools and languages e.g. JavaScript
• Good communication skills, both verbal and written.

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Job Responsibilities:

• A successful candidate will get opportunity in building quantitative analytics tools for market impact cost modeling, algo/signals research or automated management of portfolio risk and equites market microstructure. The candidate will use their knowledge of statistics to design, implement and validate models. They will use their strong programming capability to collect, manipulate and aggregate large data sets and develop production ready components. They will use their presentation skills to summarize model outcomes and write up compelling analytical notes. Data types that the team works with include ultra-high frequency market trade and quote (TAQ) data and electronic trading order and execution data.
• This data is used to develop:
• Algorithmic trading analytics
• Microstructure/signals research
• Pre-trade cost and risk estimates
• Portfolio risk optimizer
• Quantitative research for internal and external publications
• Transaction Cost Analysis (TCA) and improvement of strategy performance
• Bespoke quantitative research for EE clients
• Candidates will have the opportunity to work with stochastic models to write calibration processes, create automation tools and back test trading ideas.  The right candidates will interact with trading desks to automat process to reduce operation risk and achieve greater efficiency.
• Candidates will need to be able to work within tech prescribed infrastructure with calibration across teams with a common goal.

Apply Before the link Expires.

Apply Now: Link

Note: Shortlisted candidates will receive an email/call for the further interview process.

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