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Backtesting Jobs in Florida (NOW HIRING)

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... backtesting appropriate to production decisioning • Design optimization, recommendation, simulation, or scenario-planning engines that translate predictions into actions, constraints, tradeoffs ...

Develop and implement market risk model methodologies, algorithms, and diagnostic tools, integrating quantitative risk measuring work (including backtesting, sensitivity analysis, assumption analysis ...

Develop internal processes and artifacts for the corporate development function, including data-driven backtesting of acquisition performance against the original investment thesis. * Qualitative and ...

Develop internal processes and artifacts for the corporate development function, including data-driven backtesting of acquisition performance against the original investment thesis. * Qualitative and ...

Backtesting information

What are the key skills and qualifications needed to thrive as a Backtesting Analyst, and why are they important?

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 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 are some 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 job categories do people searching Backtesting jobs in Florida look for? The top searched job categories for Backtesting jobs in Florida are:
Sr. Data Scientist: $90/hr

Sr. Data Scientist: $90/hr

Infonet Consulting Group, Inc.

Miami, FL • On-site

$83 - $90/hr

Full-time

Posted 22 days ago

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Job description

One of Infonet's premier clients has an opening for a Sr. Data Scientist. (0513-1)

TERMS: Contract (Eligible for Conversion to Hire)
RATE: $90/hr 1099/Corp-to-Corp or $83/hr-W2

JOB LENGTH: 6-12 Months

SCOPE OF WORK

• Seeking a Sr. Data Scientist to serve as a senior owner for production data science outcomes, combining advanced modeling, experimentation, optimization, and stakeholder leadership to deliver measurable value across business processes across the company.
• Accountable for senior independent model ownership, cross-functional influence and expected to operate at the level of 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains, with influences experimentation cost, model operating cost, and build-versus-buy recommendations for owned work.
• The role differentiates Data Science ownership of problem framing, model behavior, experimentation, value measurement, adoption, and production model health from AI Engineering ownership of scalable platform foundations.
• Sr. Data Scientist will partner closely with domain leaders, product owners, AI engineers, data engineers, and senior business stakeholders to convert analytical rigor into decisions, workflow change, and measurable performance improvement

RESPONSIBILITIES

• Own problem framing for 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains by quantifying baselines, decision points, adoption paths, and expected value before modeling begins, with outcomes tied to multi-process improvements in revenue, cost, service, capacity, personalization, or operational decision quality
• Develop and validate high-performing predictive models using Python, scikit-learn, XGBoost, LightGBM, CatBoost, Databricks, feature stores, and robust backtesting appropriate to production decisioning
• Design optimization, recommendation, simulation, or scenario-planning engines that translate predictions into actions, constraints, tradeoffs, and measurable operational or commercial lift
• Build GenAI use cases with GPT-class models, Azure AI Foundry, RAG, embeddings, prompt libraries, evaluation harnesses, and safety tests, focusing on business process improvement rather than novelty
• Lead experimentation strategy using A/B tests, causal inference, quasi-experimental designs, bootstrap methods, and sensitivity analysis to prove whether interventions drive incremental value
• Create trust mechanisms using SHAP, counterfactual analysis, model cards, residual/error analysis, human review loops, and stakeholder-ready narratives that expose limitations and decision implications
• Partner with AI Engineering to productionize models through Databricks, Azure ML, MLflow, APIs, batch scoring, or containerized services while maintaining ownership of model quality, value, and adoption
• Own post-launch model health by monitoring accuracy, drift, calibration, bias, adoption, financial KPIs, latency, and cost, then driving retraining, rollback, or operating-process changes
• Lead cross-functional adoption with business, product, operations, AI Engineering, and data engineering teams so model outputs become decisions, workflow changes, and measurable performance improvements

REQUIRED SKILLS / EXPERIENCE

• Proven experience at senior scope delivering 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains, including production use, stakeholder adoption, value tracking, model operations, and measurable improvement in business outcomes
• Advanced experience with Python, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch/TensorFlow where relevant, model evaluation, hyperparameter tuning, backtesting, and feature engineering
• Strong experience applying MILP, simulation, dynamic programming, heuristics, stochastic methods, or prescriptive analytics to constrained, high-value business decisions
• Advanced experience with Azure AI Foundry, GPT-class models, RAG quality measurement, embeddings, prompt/version control, evaluation, safety testing, and workflow automation
• Deep hands-on experience with Databricks, Spark, SQL, feature stores, data quality checks, reproducibility patterns, and large-scale analytical pipelines
• Advanced experience with MLflow, Azure ML, model registries, CI/CD gates, monitoring, retraining triggers, rollback plans, and production ownership routines
• Strong production-oriented Python discipline, including modular code, testing, Git workflows, packaging, APIs, documentation, and collaboration with AI Engineering on scalable deployment patterns
• Executive-ready communication skills tailored to domain leaders, product owners, AI engineers, data engineers, and senior business stakeholders, with the ability to translate

PREFERRED EDUCATION

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Operations Research, Engineering, Economics, or related quantitative field, or equivalent experience delivering production models at comparable scale

TRAVEL REQUIREMENT

• Yes – This position may require some domestic or international travel

** No 3rd party vendors ** Unable to sponsor H1-B visas **

Please refer to position: 051426SDS - Sr. Data Scientist: $90/hr in the subject line of all correspondence.

Company Description

Infonet is an Information Technology staffing firm based in South Florida. The company was founded over 20 years ago by IT Hiring Managers and Software/Hardware Engineers.
What distinguishes Infonet is our passion to serve you. We take your job search seriously, not ourselves.