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Data Science Trading Jobs (NOW HIRING)

FanDuel

Risk Data Science, Director

Jersey City, NJ

... Data Science Director to help shape our Risk & Trading team at America's #1 Sportsbook. The role ... You will be a key member of the Risk & Trading leadership team , partnering closely with Trading ...

Lemonade

Data Science Manager

New York, NY · On-site

$210K - $230K/yr

Unlike most publicly traded companies, we're nimble and efficient. We take pride in the fact that ... You'll sit within our Data Science team, owning the direction of a group of talented data ...

Microsoft

Data Science Strategy Lead

Redmond, WA · On-site

Communicate strategy, trade offs, progress, and impact clearly to senior and executive stakeholders ... Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research ...

Rundoo

Data Science Intern

Chicago, IL · On-site

$1K/wk

What you'll do as a Data Science Intern at Rundoo * Analyze data on product, sales and GTM ... Nick (CEO): studied math & computer science at Stanford; worked as a trader at Bridgewater ...

Chewy

Associate Director, Data Science

Bellevue, WA · On-site

Communicate complex technical concepts, business insights, trade-offs, and recommendations ... D.) in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field ...

Chewy

Associate Director, Data Science

Boston, MA

Communicate complex technical concepts, business insights, trade-offs, and recommendations ... D.) in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field ...

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Data Science Trading information

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$24K

$115.8K

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How much do data science trading jobs pay per year?

As of Jun 12, 2026, the average yearly pay for data science trading in the United States is $115,802.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,000.00 and $159,000.00 per year, depending on experience, location, and employer.

What is the 80 20 rule in data science?

In data science trading, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or data features. Data scientists often use this rule to focus on the most impactful variables or models to optimize performance efficiently.

What are the key skills and qualifications needed to thrive in the Data Science Trading position, and why are they important?

To thrive in Data Science Trading, you need strong quantitative analysis, statistical modeling, and programming skills, usually supported by a degree in a quantitative field like mathematics, finance, or computer science. Proficiency in Python, R, SQL, and experience with machine learning frameworks and trading platforms such as Bloomberg or QuantConnect are commonly required. Excellent problem-solving, collaboration, and the ability to communicate complex concepts clearly are standout soft skills. These capabilities are crucial for building, optimizing, and explaining data-driven trading strategies in fast-paced financial environments.

What is a Data Science Trading job?

A Data Science Trading job involves using data analysis, machine learning, and statistical modeling to develop trading strategies and optimize financial decision-making. Professionals in this field work with large datasets, build predictive models, and implement algorithms to identify market patterns and trading opportunities. They collaborate with traders and quantitative analysts to enhance trading performance and manage risk. Strong programming skills in Python, R, or SQL, along with expertise in finance and mathematics, are essential for success in this role.

What are some typical responsibilities and daily tasks for professionals working in Data Science Trading?

Data Science Trading professionals typically analyze large financial datasets, develop algorithmic trading models, and monitor the performance of existing strategies. A typical day might include collaborating with traders and engineers, implementing new statistical techniques or machine learning algorithms, and backtesting strategies against historical market data. Routine tasks also involve writing code to automate processes, conducting risk assessments, and presenting insights to stakeholders. This role is highly collaborative and requires adapting to rapidly changing market conditions, making each day dynamic and intellectually challenging.

Is data science useful for trading?

Data science is highly useful for trading, as it enables analysts and traders to develop predictive models, identify market patterns, and make data-driven decisions. Skills in machine learning, statistical analysis, and programming tools like Python or R are commonly applied in trading environments to improve strategies and optimize performance.

How much do NYSE data scientists make?

Data scientists working in financial trading environments, such as at the NYSE, typically earn between $90,000 and $150,000 annually, depending on experience, education, and skill set. Senior roles or those with specialized skills in machine learning and quantitative analysis can earn higher salaries, often exceeding $200,000 with bonuses and incentives.

Is 40 too late for data science?

Data science trading roles are open to candidates of various ages, and starting a career at 40 is feasible with relevant skills in programming, statistics, and data analysis. Many professionals successfully transition into data science later in their careers by gaining certifications and practical experience.
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Data Science Trading jobs near you
Director - Data Science Consulting

Director - Data Science Consulting

Tiger Analytics Inc.

California City, CA • On-site

Full-time

Posted 6 days ago

Job description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.

We are looking for an Director of Data Science to lead high-impact applied ML and analytics initiatives. This role combines deep technical expertise, strong experimentation rigor, and business leadership to influence product direction and drive measurable outcomes at scale.

Responsibilities:

  • Own and drive end-to-end data science workstreams from problem definition to production and impact measurement.
  • Build and scale statistical and ML models for personalization, recommendations, growth optimization, fraud, and experimentation platforms.
  • Partner closely with Product, Engineering, Marketing, and Leadership to define success metrics, trade-offs, and roadmaps.
  • Design and maintain production ML pipelines using Python, SQL, Airflow, and modern data tooling.
  • Collaborate with client stakeholders to translate business needs into high-level analytical solution designs.
  • Present insights and solutions to business leaders, demonstrating impact and value.
  • Manage analytics projects and coordinate with global client and Tiger teams.
  • Lead requirement discussions, and oversee planning, development, and documentation of DS/AI solutions.
  • Partner with technical teams to select appropriate analytical methods and generate actionable insights.
  • Communicate results to senior leadership and support the operationalization of analytics solutions.

Requirements

  • 15+ years of professional experience in Data Science, Applied ML, or Advanced Analytics, with leadership at scale..
  • Must have experience working on traditional ML Models. Knowledge of ML frameworks like Scikitlearn, Tensorflow, and Keras.
  • Strong hands-on expertise in Python, SQL, and statistical modeling.
  • Familiarity with data orchestration and workflows (Airflow, Git-based CI/CD, Fivetran).
  • Strong understanding of cloud-native data and ML platforms (AWS, GCP, Azure).
  • Excellent communication skills with the ability to influence Director+ stakeholders.
  • Identify and implement improvements to analytics workflows and processes to enhance efficiency and effectiveness.
  • Ensure all analytical activities adhere to guidelines, regulatory requirements, and industry standards.
  • Ability to engage with executive/VP-level stakeholders from the client's team to translate business problems into high-level analytics solution approaches.
  • A solid understanding of statistical and machine-learning algorithms is a plus.
  • Bachelor's in Business Analytics or equivalent work experience.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

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