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Backtesting Jobs in Washington (NOW HIRING)

Phase2 Technology

Cybersecurity Data Scientist

Mclean, VA · On-site

$77.60 - $176/hr

Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...

Capital One

Manager, Credit Accounting

Mclean, VA

Oversee impactful ACL backtesting activities that provide transparency on performance vs. expectations, which are a critical input to quarterly ACL governance activities with Senior Leadership

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Backtesting information

What is backtesting?

Backtesting is the process of evaluating a trading strategy or investment model by applying it to historical market data. This helps traders and analysts see how the strategy would have performed in the past, which can provide insights into its potential effectiveness and risks. While backtesting can help identify strengths and weaknesses, it's important to remember that past performance is not always indicative of future results. The reliability of backtesting depends on data quality, strategy design, and how well it simulates real trading conditions.

What skills and qualifications are needed to thrive as a backtesting analyst?

To thrive as a Backtesting Analyst, you need a strong background in quantitative analysis, statistics, programming (typically in Python or R), and familiarity with financial markets, usually supported by a degree in mathematics, finance, or a related field. Proficiency with backtesting platforms (such as QuantConnect or Zipline), data analysis tools, and version control systems like Git is often required. Attention to detail, critical thinking, and strong problem-solving abilities are key soft skills that help ensure robust model evaluation and development. These skills are vital for accurately assessing trading strategies and minimizing risk in real-world financial applications.

What are common challenges faced when backtesting trading strategies, and how can they be managed?

One common challenge in backtesting trading strategies is the risk of overfitting, where a model performs exceptionally well on historical data but fails in live markets. Data quality and availability can also pose issues, as incomplete or inaccurate data may skew results. To manage these challenges, it's important to use out-of-sample testing, robust data cleaning processes, and to validate strategies on multiple datasets. Collaborating with quantitative analysts and developers can also help ensure the backtesting process is thorough and reliable.

What is the difference between Backtesting vs Quantitative Analyst?

AspectBacktestingQuantitative Analyst
Primary RoleTesting trading strategies using historical dataDeveloping and implementing quantitative models for investment decisions
Required SkillsData analysis, programming, finance knowledgeMathematics, programming, financial theory
Work EnvironmentTrading firms, hedge funds, financial institutionsAsset management firms, hedge funds, banks
CertificationsOften none required, but CFA or CQF helpfulCFA, CQF, or advanced degrees common

Backtesting focuses on evaluating trading strategies with historical data, while a Quantitative Analyst develops models to inform investment decisions. Both roles require strong analytical skills and finance knowledge but differ in scope and responsibilities.

What are popular job titles related to Backtesting jobs in Washington?

For Backtesting jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Backtesting jobs in Washington look for?

The top searched job categories for Backtesting jobs in Washington are:

Backtesting jobs near you

Senior Data Scientist - Payload & SAT-RAN

AST SpaceMobile

Lanham, MD • On-site

Full-time

Posted 8 days ago

Job description

AST SpaceMobile is building the first and only global cellular broadband network in space to operate directly with standard, unmodified mobile devices based on our extensive IP and patent portfolio and designed for both commercial and government applications. Our engineers and space scientists are on a mission to eliminate the connectivity gaps faced by today's five billion mobile subscribers and finally bring broadband to the billions who remain unconnected.
Position Overview
The Senior Data Scientist - Payload & SAT-RAN defines, builds, and operationalizes SAT-RAN data models across Payload, Gateway (GW), and Cellular/RAN subsystems. This role focuses on creating robust telemetry-driven models and metrics that quantify system performance, detect and prevent failures and anomalies, and enable data-driven optimization of user experience, capacity, and duty cycle under various ground and in-orbit constraints. This person works closely with payload engineering, gateway/network engineering, RAN/system architects, and operations to translate complex, multi-domain data into actionable insights and production-grade analytics.
Key Responsibilities:
  • Own Payload/GW/SAT-RAN data strategy: define key subsystem metrics, data sources, and collection requirements spanning payload, gateway, transport, and RAN/service layers.
  • Design and maintain SAT-RAN subsystem data models (entities, relationships, identifiers, etc.) to unify telemetry across domains and support scalable analytics.
  • Lead data model definition and deployment into production systems, including instrumentation requirements, pipeline design, validation, versioning, and data quality monitoring.
  • Develop performance analytics for end-to-end Satellite-RAN performance projected across multiple subsystems, including attribution of impact across payload, GW, transport, core, and RAN layers.
  • Build failure/anomaly detection and prevention systems using multivariate time-series, correlation/causality-informed approaches, topology/context-aware features, and alert deduplication/triage scoring.
  • Create qualitative quality scoring models that combine predictive signals with measured KPIs/KQIs, including confidence/uncertainty measures.
  • Develop fleet service scheduling models linking orbital state/visibility, predicted SAT-RAN capacity and quality, interference, and spacecraft power/thermal constraints to achievable service performance and demand fulfillment.
  • Build decision-support data products: dashboards, health scores, early-warning indicators, incident enrichment, and executive-ready reporting for SAT-RAN performance.
  • Partner with engineering teams to define success metrics, run backtesting/regression evaluation, and operationalize models into workflows such as assurance, release validation, and optimization loops.
  • Establish best practices for reproducibility and MLOps, including model monitoring, drift detection, dataset/version governance, and documentation.

Qualifications
Education:
Bachelor's or Master's degree in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics, or a related technical field, or equivalent experience.
Experience:
  • 5+ years of experience delivering data science/ML solutions in production, including monitoring, anomaly detection, forecasting, quality scoring, or optimization support.
  • Strong proficiency in Python (pandas, NumPy, scikit-learn, and time-series tooling) and strong SQL skills.
  • Demonstrated experience defining and operating data models and analytics pipelines, including schemas, identifiers, aggregation logic, data validation, and lineage.
  • Strong statistical foundations, including model evaluation, uncertainty, time-series behavior, bias/variance, and backtesting.
  • Ability to translate cross-domain system problems into measurable metrics and deployable analytics.

Preferred Qualifications:
  • Experience with multivariate anomaly detection at scale, including change-point detection, sequence models where justified, and graph/topology-aware features.
  • Telecom/systems experience, including LTE/5G KPIs/KQIs, OSS counters/alarms, QoE/QoS metrics, and RAN performance indicators.
  • Familiarity with scheduling/capacity modeling concepts, including resource allocation, interference-aware capacity, constraint modeling, and power/thermal-limited regimes.
  • MLOps experience, including deployment, monitoring, drift detection, and CI/CD for data and models.

Soft Skills:
Technology Stack:
  • Python, including pandas, NumPy, scikit-learn, and time-series analysis libraries.
  • SQL and relational/analytics data stores.
  • Multivariate time-series and anomaly/change-point detection frameworks.
  • Telemetry, OSS counters/alarms, and KPI/KQI data pipelines.
  • MLOps tooling for model monitoring, drift detection, versioning, and CI/CD.
  • Dashboarding and reporting platforms for health scores and executive-facing analytics.

Physical Requirements
  • Ability to lift up to 25 lbs.
  • Ability to use a computer for extended periods.
  • Ability to work in a standard office environment.
  • Ability to travel occasionally to support cross-team collaboration or reviews with engineering and operations teams as needed.

This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands.
AST SpaceMobile is an Equal Opportunity, at will Employer; employment is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

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