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채용Morgan Stanley

FID, Quantitative Trading Desk Strategist

Morgan Stanley

FID, Quantitative Trading Desk Strategist

Morgan Stanley

New York, New York, United States of America

·

On-site

·

Full-time

·

1mo ago

필수 스킬

Python

React

Go

Machine Learning

The Fixed Income Division is comprised of Interest Rate and Currency Products, Credit Products and Distribution. Professionals in the Division assess and actively manage risk, trade securities, and structure as well as execute innovative transactions in the fast-paced and constantly changing global markets. Morgan Stanley aligns its municipal investment banking, underwriting, sales, trading, lending, and M&A advisory in one integrated organizational group under the umbrella of the Municipal Securities Division.

Imagine you run a grocery store.

Every decision costs money. Too many carrots? Waste. Too few? Missed profit. Discount too early? You leave money on the table. Discount too late? You’re stale bread into the trash.

Now imagine this grocery store trades bonds, derivatives, and ETFs not bananas.

We don’t worry about spoiled produce — but we do worry about inventory risk, market impact, and whether today’s profits makes sense given the decisions we made yesterday. Prices move. Competitors react. Models lie (sometimes). Data tells stories — if you know how to listen.

That’s where you come in.

You’ll sit on a trading desk and help answer questions like:

  • How much inventory should we carry right now?

  • Which positions are quietly printing money, and which ones are fooling us?

  • What happens to P&L if yields move, volatility spikes, or the guy across the street starts dumping inventory?

  • Are we actually being smart… or just getting lucky?

You’ll build models, measure performance, explain results, and influence daily trading decisions — not from a distance, but in real time, with real consequences.

What We're Looking For

  • Master’s Mathematics, Physics, Engineering, or something similarly quantitative

  • Strong Python skills — you can go from idea → code

  • Solid grounding in probability, statistics, and numerical modeling

  • You enjoy telling stories with data

  • Familiarity with bonds, ETFs, and derivatives (or the ability to learn them fast)

  • You’re comfortable having your ideas questioned — and questioning others

  • A genuinely good sense of humor (not optional)

Nice to Have

  • kdb/q experience

  • Machine learning techniques like random forests or logistic regression

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.

To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices​ into your browser.

Expected base pay rates for the role will be between $150,000 - $200,000 per year for Associate and**$225,000 - $250,000** **for Vice President, at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.

Morgan Stanley's goal is to build and maintain a workforce that is diverse in experience and background but uniform in reflecting our standards of integrity and excellence. Consequently, our recruiting efforts reflect our desire to attract and retain the best and brightest from all talent pools. We want to be the first choice for prospective employees.

It is the policy of the Firm to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, creed, age, sex, sex stereotype, gender, gender identity or expression, transgender, sexual orientation, national origin, citizenship, disability, marital and civil partnership/union status, pregnancy, veteran or military service status, genetic information, or any other characteristic protected by law.

Morgan Stanley is an equal opportunity employer committed to diversifying its workforce (M/F/Disability/Vet).

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Morgan Stanley 소개

Morgan Stanley

Morgan Stanley is an American multinational investment bank and financial services company headquartered at 1585 Broadway in Midtown Manhattan, New York City.

10,001+

직원 수

New York

본사 위치

$150B

기업 가치

리뷰

3.2

10개 리뷰

워라밸

2.5

보상

2.8

문화

3.8

커리어

3.2

경영진

3.5

45%

친구에게 추천

장점

Nice and welcoming people/coworkers

Good career foundation and growth opportunities

Great management and benefits

단점

Limited conversion to full-time positions

Poor compensation for junior employees

High turnover and branch politics

연봉 정보

6,255개 데이터

Junior/L3

Mid/L4

VP

Intern

Director

Junior/L3 · Analyst

40개 리포트

$109,500

총 연봉

기본급

$100,000

주식

-

보너스

-

$83,048

$227,500

면접 경험

5개 면접

난이도

3.2

/ 5

소요 기간

21-35주

경험

긍정 0%

보통 80%

부정 20%

면접 과정

1

Application Review

2

HR Screen/HireVue

3

Technical Interview

4

Superday/Final Round

5

Offer Decision

자주 나오는 질문

Technical Knowledge

Behavioral/STAR

Finance/Investment Concepts

Case Study

Culture Fit