トレンド企業

Morgan Stanley
Morgan Stanley

Leading company in the financial services industry

FID, Front Office Derivatives Quant

職種データサイエンス
経験ミドル級
勤務地New York, United States
勤務オンサイト
雇用正社員
掲載2ヶ月前
応募する

必須スキル

Python

Java

The Fixed Income Division is comprised of Interest Rates and Currency Products, Credit Products, and Distribution. Professionals in the Division assess and actively manages risk, trade securities, and structure as well as execute innovative transactions in the fast-paced and constantly changing global markets. The Commodities Division is a market leader in energy, metals, and agricultural product trading worldwide whose professionals trade in both physical and derivative commodity risk.

We are a front-office quantitative trading team focused on pricing, risk management, and trading of derivatives. Our work sits at the intersection of stochastic analysis, optimization, and real time algorithmic trading.

This role is for a researcher who enjoys developing mathematical ideas into working production models.

What You’ll Work On

  • Develop and refine derivative pricing models using stochastic calculus and probability theory, with a focus on how the hedging works

  • Design and implement algorithmic trading strategies that coordinate derivatives and their underlying instruments across short time horizons (days to weeks)

  • Apply stochastic optimal control to dynamic portfolio optimization and risk management

  • Work closely with traders to translate mathematical insight into trading decisions and iterate rapidly based on market feedback.

  • Build robust, production-quality research code in Python (and occasionally Java)

Why This Role Is Different

  • You will work on open-ended problems where the “right” model is not known in advance.

  • Your work will have direct and visible impact on trading outcomes.

  • You will have the freedom to challenge assumptions, propose new approaches, and own your models end-to-end.

  • You’ll collaborate with other mathematically rigorous researchers in a setting that values curiosity and integrity

Required Qualifications

  • Ph.D. in Mathematics, Operations Research, or a closely related field

  • Strong foundation in stochastic calculus

  • Good sense of Humor

  • Proficiency in Python

Nice to Have

  • Background in stochastic control or optimization

  • Experience with derivative modeling

  • Familiarity with Java

  • Prior exposure to algorithmic trading

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について

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+

従業員数

New York

本社所在地

$150B

企業価値

レビュー

10件のレビュー

4.1

10件のレビュー

ワークライフバランス

2.8

報酬

4.2

企業文化

3.7

キャリア

4.1

経営陣

2.9

75%

知人への推奨率

良い点

Great learning opportunities and experience

High salary and bonuses

Good team dynamics and supportive colleagues

改善点

Long hours during peak times

High stress and overwhelming environment

Work-life balance issues

給与レンジ

6,221件のデータ

Junior/L3

Senior/L5

Staff/L6

Junior/L3 · Data Scientist L3

0件のレポート

$130,639

年収総額

基本給

-

ストック

-

ボーナス

-

$111,043

$150,235

面接レビュー

レビュー6件

難易度

3.2

/ 5

期間

21-35週間

面接プロセス

1

Application Review

2

HR Screen/HireVue

3

Technical/Behavioral Interviews

4

Superday/Final Round

5

Onsite Interview

6

Offer Decision

よくある質問

Technical Knowledge

Behavioral/STAR

Investment/Finance Concepts

Case Study

Culture Fit