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Data Science Trading Jobs (NOW HIRING)

Senior Analyst, Data Science

Fort Mill, SC · On-site

$75K - $95K/yr

Evaluate model performance using appropriate metrics and clearly communicate trade-offs ... Contribute to building a scalable data science practice by identifying opportunities to improve ...

The Crown Is Yours As a Data Science Manager, you drive strategy and execution to power ... Join Our Team We're a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As ...

Evaluate model performance using appropriate metrics and clearly communicate trade-offs ... Contribute to building a scalable data science practice by identifying opportunities to improve ...

Guide data pipeline architecture from a data science perspective, with a focus on robustness ... Demonstrated ability to make informed trade-offs regarding model selection, implementation ...

Guide data pipeline architecture from a data science perspective, with a focus on robustness ... Demonstrated ability to make informed trade-offs regarding model selection, implementation ...

Senior Data Science Engineer

Boston, MA · On-site

$115K - $156K/yr

You'll partner with Quantitative Traders, Machine Learning Engineers, Product Managers, and cross ... What you'll do as a Senior Data Science Engineer * Lead the design, development, and validation of ...

Preferred : • Experience building or growing high-performing data science teams in production-aware or high-stakes settings. • Exposure to financial markets, trading systems, or quantitative ...

... trading platform, client experience, and business operations. Reporting to the Sr. Director of AI, Data Science amp; Enterprise Data, this role will own the full data science lifecycle -- from ...

... trading platform, client experience, and business operations. Reporting to the Sr. Director of AI, Data Science & Enterprise Data, this role will own the full data science lifecycle - from ...

Design and execute scenario analysis to evaluate trade-offs related to demand variability, labor ... data pipelines, and support system enhancements that enable scalable modeling and planning ...

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Data Science Trading information

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$24K

$115.8K

$211K

How much do data science trading jobs pay per year?

As of Aug 15, 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 are the key skills and qualifications needed to thrive in data science trading?

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?

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 does a data science trading do?

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 good for trading?

Data science is valuable in trading roles for analyzing large datasets, developing predictive models, and automating decision-making processes. Skills in programming, statistics, and machine learning are essential, and professionals often use tools like Python, R, and SQL to support trading strategies. Success in this field depends on strong analytical abilities and understanding financial markets.
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Infographic showing various Data Science Trading job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $115,802 per year, or $55.7 per hour.

Head of Data Science - Identity & Compliance

Socure

Miami, NM • On-site

Full-time

Re-posted 8 days ago


Job description

Job Summary:
Socure is building the identity trust infrastructure for the digital economy, and they are seeking a Head of Data Science to lead the development of AI-powered identity and compliance solutions. This role involves owning the data science vision, leading a high-performing team, and driving innovation in machine learning and compliance strategies.
Responsibilities:
• Own and drive the data science vision for Identity & Compliance, delivering measurable improvements in accuracy, coverage, latency, and customer impact across all products in scope.
• Lead, build, and develop a high-performing team of data scientists and applied researchers, fostering a culture of technical excellence, speed, and accountability.
• Architect and deploy advanced machine learning systems across identity verification, entity resolution, sanctions screening, and compliance risk modeling.
• Drive the development of graph-based intelligence, including large-scale graph neural networks (GNNs) and link analysis models to power Identity Graph, fraud detection, and watchlist matching.
• Lead the design and implementation of agent-based AI systems, enabling automated decisioning, case triage, investigation workflows, and adaptive compliance strategies.
• Advance state-of-the-art modeling approaches, including deep learning, representation learning, graph learning, and multimodal fusion across structured and unstructured data.
• Drive innovation in Identity Graph and Prefill systems, improving identity resolution, linking, and enrichment capabilities across global datasets.
• Partner closely with Product, Engineering, Risk, and Go-to-Market teams to translate business needs into scalable AI solutions that deliver customer value.
• Own the end-to-end model lifecycle, including data strategy, feature engineering, model development, evaluation, deployment, and monitoring.
• Ensure alignment with regulatory and compliance requirements, balancing model performance with explainability, auditability, and governance.
• Continuously raise the bar on experimentation and execution velocity, enabling rapid iteration while maintaining high standards for reliability and impact.
• Own customer communication and stakeholder management, serving as a trusted technical leader in engagements with customers, partners, and internal stakeholders; clearly articulate model behavior, agentic systems, performance trade-offs, and roadmap decisions while building strong, long-term relationships.
• Represent Socure externally as a thought leader in identity, compliance, and applied AI.
Qualifications:
Required:
• Advanced degree (MS/PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10+ years of experience in data science and machine learning, with a strong track record of delivering production-grade AI systems at scale.
• Significant experience in identity verification, KYC/AML, fraud detection, or risk modeling in fintech or adjacent domains.
• Proven leadership experience managing and scaling high-performing data science teams.
• Deep expertise in modern machine learning techniques, including deep learning, graph neural networks, entity resolution, and large-scale data systems.
• Strong understanding of agentic system design, including agent skills, orchestration/harness frameworks, and real-world deployment of autonomous or human-in-the-loop agents.
• Strong experience working with heterogeneous data sources, including structured data, text, network/graph data, and third-party identity signals.
• Demonstrated ability to drive ambiguous, high-impact problems to production, balancing speed, rigor, and business outcomes.
• Hands-on experience with Apache Spark and large-scale distributed data systems.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch), and familiarity with graph ML frameworks is a plus.
• Proven ability to engage with customers and external stakeholders, clearly explaining complex AI systems, trade-offs, and outcomes to both technical and non-technical audiences.
Preferred:
• Experience representing organizations in customer-facing discussions, executive briefings, or public speaking engagements is strongly preferred.
Company:
Socure is a predictive analytics platform for digital identity verification of consumers. Founded in 2012, the company is headquartered in Incline Village, USA, with a team of 501-1000 employees. The company is currently Late Stage.