Asset & Wealth Management - Quantitative Strategist - Associate - Dallas
Goldman Sachs, Dallas, TX, United States
About Goldman Sachs Wealth Management
Across Wealth Management, Goldman Sachs helps empower clients and customers around the world to reach their financial goals. Our advisor‑led wealth management businesses provide financial planning, investment management, banking, and comprehensive advice to a wide range of clients, including ultra‑high net worth and high net worth individuals, as well as family offices, foundations and endowments, and corporations and their employees. Our consumer business provides digital solutions for customers to better spend, borrow, invest, and save. Across Wealth Management, our growth is driven by a relentless focus on our people, our clients and customers, and leading‑edge technology, data, and design. Our quantitative strategists are at the cutting edge of our business and solve real‑world problems through a variety of analytical methods. As a member of our team, you will utilize your training in mathematics, programming, and logical thinking to build quantitative models that drive success in our business. Your problem‑solving talents and aptitude for innovation will help define your contributions and enable you to find solutions to a broad range of problems, in a dynamic, fast‑paced environment.
Job Description
Responsibilities
Developing and deploying ML models for fraud and anomaly detection as well as business workflows enhancement
Delivering risk metrics and quantitative analytics for financial and non-financial risks across wealth management
Develop AI‑led solutions to improve efficiency and accuracy in risk management.
Building and maintaining robust and systematic risk management tools and reporting
Collaborating on the design of new and existing strategies to address clients’ investment goals.
Developing and maintaining risk management and portfolio analysis tools across multiple asset classes for senior management and portfolio managers.
Building and maintaining infrastructure of Strategists’ analytical systems.
Basic Qualifications
Bachelor, Masters or Ph.D. in a quantitative or engineering field, e.g. mathematics, physics, quantitative finance, computational finance, computer science, engineering
1-3 years of experience in the job offered or related quantitative financial modeling and software development positions
Programming and mathematical skills are required
Creativity, problem‑solving skills, and ability to communicate complex ideas to a variety of audiences
A self‑starter, should have ability to work independently as well as thrive in a team environment
Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
Experience with building models using common data science toolkits, i.e., Python (Pandas, NumPy, Scikit‑learn) and Spark
Experience with prompt engineering, working with LLM models, and MCP.
Previous work experience in: Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL
Preferred Qualifications
Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
Experience with building models using common data science toolkits, i.e., Python (Pandas, NumPy, Scikit‑learn) and Spark
Experience with prompt engineering, working with LLM models, and MCP.
Previous work experience in: Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL
About Goldman Sachs
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2021. All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity
#J-18808-Ljbffr
Across Wealth Management, Goldman Sachs helps empower clients and customers around the world to reach their financial goals. Our advisor‑led wealth management businesses provide financial planning, investment management, banking, and comprehensive advice to a wide range of clients, including ultra‑high net worth and high net worth individuals, as well as family offices, foundations and endowments, and corporations and their employees. Our consumer business provides digital solutions for customers to better spend, borrow, invest, and save. Across Wealth Management, our growth is driven by a relentless focus on our people, our clients and customers, and leading‑edge technology, data, and design. Our quantitative strategists are at the cutting edge of our business and solve real‑world problems through a variety of analytical methods. As a member of our team, you will utilize your training in mathematics, programming, and logical thinking to build quantitative models that drive success in our business. Your problem‑solving talents and aptitude for innovation will help define your contributions and enable you to find solutions to a broad range of problems, in a dynamic, fast‑paced environment.
Job Description
Responsibilities
Developing and deploying ML models for fraud and anomaly detection as well as business workflows enhancement
Delivering risk metrics and quantitative analytics for financial and non-financial risks across wealth management
Develop AI‑led solutions to improve efficiency and accuracy in risk management.
Building and maintaining robust and systematic risk management tools and reporting
Collaborating on the design of new and existing strategies to address clients’ investment goals.
Developing and maintaining risk management and portfolio analysis tools across multiple asset classes for senior management and portfolio managers.
Building and maintaining infrastructure of Strategists’ analytical systems.
Basic Qualifications
Bachelor, Masters or Ph.D. in a quantitative or engineering field, e.g. mathematics, physics, quantitative finance, computational finance, computer science, engineering
1-3 years of experience in the job offered or related quantitative financial modeling and software development positions
Programming and mathematical skills are required
Creativity, problem‑solving skills, and ability to communicate complex ideas to a variety of audiences
A self‑starter, should have ability to work independently as well as thrive in a team environment
Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
Experience with building models using common data science toolkits, i.e., Python (Pandas, NumPy, Scikit‑learn) and Spark
Experience with prompt engineering, working with LLM models, and MCP.
Previous work experience in: Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL
Preferred Qualifications
Excellent understanding of machine learning techniques and algorithms, such as gradient boosting decision trees, random forests, etc., is a plus
Experience with building models using common data science toolkits, i.e., Python (Pandas, NumPy, Scikit‑learn) and Spark
Experience with prompt engineering, working with LLM models, and MCP.
Previous work experience in: Utilizing statistical methods, including time‑series and regression analysis; programming in object‑oriented languages for efficient model implementations; manipulating data sets using relational databases and SQL
About Goldman Sachs
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2021. All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity
#J-18808-Ljbffr