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New Grad Quant Jobs in Colorado (NOW HIRING)

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New Grad Quant information

What is a new grad quant?

A New Grad Quant, short for 'Quantitative Analyst,' is an entry-level position for recent graduates in finance, mathematics, computer science, or related fields. These professionals use mathematical models, statistical techniques, and programming skills to analyze financial data and develop trading strategies. New Grad Quants typically work at investment banks, hedge funds, or financial technology firms, where they support senior quants in research, risk management, and portfolio optimization. The role often involves a steep learning curve and requires strong analytical thinking and problem-solving abilities.

What are the key skills and qualifications needed to thrive as a new grad quant?

To thrive as a New Grad Quant, you need a strong background in mathematics, statistics, and programming, typically supported by a degree in quantitative fields such as mathematics, physics, engineering, or computer science. Proficiency with tools like Python, R, MATLAB, and familiarity with data analysis libraries or financial modeling systems is expected. Analytical thinking, attention to detail, and effective communication are crucial soft skills for collaborating with teams and interpreting complex data. These competencies enable new quants to develop robust models, contribute valuable insights, and adapt quickly in the fast-paced finance industry.

What are some typical challenges new graduate quants face when transitioning from academia to a professional finance environment?

New graduate quants often encounter challenges such as adapting to the fast-paced nature of financial markets, learning to apply theoretical knowledge to real-time problems, and navigating large codebases or proprietary platforms. Collaboration is key, as quants frequently work in cross-functional teams with traders, developers, and risk managers, requiring strong communication skills. Additionally, new grads must prioritize continuous learning to keep up with evolving models and technologies, while managing deadlines and performance expectations.

What is the difference between New Grad Quant vs Quant Analyst?

AspectNew Grad QuantQuant Analyst
Required CredentialsDegree in Math, Finance, or Computer Science; internships preferredAdvanced degree often preferred; experience in modeling and programming
Work EnvironmentEntry-level, training-focused, collaborative teamsMore independent, project-driven, client-facing
Employer & Industry UsageFinancial firms, hedge funds, banksSame as New Grad Quant, with increased responsibilities

The main difference between a New Grad Quant and a Quant Analyst lies in experience and responsibility. New Grad Quants are entry-level, focusing on learning and supporting teams, while Quant Analysts have more experience, handling complex models and client interactions. Both roles require strong quantitative skills, but the Quant Analyst role typically demands a deeper understanding and proven track record.

What are popular job titles related to New Grad Quant jobs in Colorado?

For New Grad Quant jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching New Grad Quant jobs in Colorado look for?

The top searched job categories for New Grad Quant jobs in Colorado are:

What cities in Colorado are hiring for New Grad Quant jobs?

Cities in Colorado with the most New Grad Quant job openings:

New Grad Quant jobs near you

Software Engineer I, Data Science (New Grad)

Denver, CO • On-site

True Anomaly
Guided Missile and Space Vehicle Manufacturing • 11 - 50 employees
Learn more about True Anomalyexternal

$117K - $141K/yr

Full-time

Posted 17 days ago

Key responsibilities

  • Perform exploratory data analysis on manufacturing telemetry, test logs, mission data, and on-orbit spacecraft health telemetry to identify patterns and surface anomalies

  • Build operational dashboards in Grafana or Plotly Dash showing real-time production status, spacecraft health metrics, mission performance, and anomaly alerts

  • Train basic predictive models (logistic regression, random forests) to flag at-risk components during manufacturing and predict spacecraft health degradation during missions

Job description

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors - enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
  • Be the offset. We create asymmetric advantages with creativity and ingenuity.
  • What would it take? We challenge assumptions to deliver ambitious results.
  • It's the people. Our team is our competitive advantage and we are better together.

YOUR MISSION
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You'll turn spacecraft data into actionable insights across manufacturing and operations: building dashboards that surface production bottlenecks and on-orbit anomalies, analyzing test failures and mission telemetry to identify root causes, training predictive models that flag at-risk components before integration and detect spacecraft health degradation during missions, and mining telemetry to catch anomalies operators would miss. Your work spans the full spacecraft lifecycle. Pre-launch, you'll analyze manufacturing telemetry, test logs, failure reports, and supplier data to catch problems before integration. Post-launch, you'll monitor on-orbit telemetry streams, detect anomalies in spacecraft health data, analyze mission performance, and flag degradation patterns that predict future failures. This is entry-level data science work supporting hardware production and spacecraft operations. You'll write SQL queries, build predictive models in Python, create operational dashboards, and see your analysis drive decisions on the manufacturing floor and in mission control.
This is a 3 month temporary employment engagement. There is potential to convert to regular employment based on performance and business need.
RESPONSIBILITIES
  • Perform exploratory data analysis on manufacturing telemetry, test logs, mission data, and on-orbit spacecraft health telemetry to identify patterns and surface anomalies
  • Build operational dashboards in Grafana or Plotly Dash showing real-time production status, spacecraft health metrics, mission performance, and anomaly alerts
  • Train basic predictive models (logistic regression, random forests) to flag at-risk components during manufacturing and predict spacecraft health degradation during missions
  • Write SQL queries to extract, join, and aggregate data from manufacturing databases, test systems, mission telemetry streams, and spacecraft health archives
  • Analyze test failures and on-orbit anomalies to identify common failure modes, cluster similar issues, and quantify impact on schedule and mission success
  • Create data visualizations (matplotlib, seaborn, Plotly) that communicate findings to engineers, manufacturing leads, mission operators, and program managers
  • Implement statistical process control charts to detect out-of-spec conditions in manufacturing processes and spacecraft telemetry before they cascade
  • Monitor on-orbit telemetry streams for anomalies: battery voltage trends, thermal behavior, attitude control health, communications link quality
  • Document analysis methodology in Jupyter notebooks enabling reproducibility and knowledge transfer across manufacturing and operations teams
  • Learn reliability engineering and mission operations concepts: failure modes, burn-in testing, on-orbit commissioning, spacecraft health monitoring, and anomaly response procedures

QUALIFICATIONS
  • Bachelor's or Master's degree in data science, statistics, industrial engineering, applied mathematics, operations research, or related quantitative field
  • Proficiency in Python for data analysis: pandas, numpy, matplotlib, seaborn
  • Working knowledge of SQL for querying relational databases: SELECT, JOIN, GROUP BY, aggregation functions
  • Coursework in statistics: hypothesis testing, regression, probability distributions, experimental design
  • Ability to create clear visualizations that communicate insights to technical and non-technical audiences
  • Strong curiosity about how things fail and how data can predict failures before they happen
  • Debugging mindset: when the model gives wrong answers or the query returns unexpected results, you dig in to find out why
  • Eagerness to learn manufacturing, operations, and reliability engineering domains where data drives real decisions
  • U.S. Citizen (required for facility access and government contracts)

PREFERRED SKILLS AND EXPERIENCE
  • Experience with machine learning in Python: scikit-learn for classification/regression, model validation, train/test splits, cross-validation
  • Familiarity with time-series analysis: plotting sensor trends, detecting change points, smoothing noisy signals
  • Exposure to data visualization tools: Grafana, Tableau, Plotly Dash, or similar dashboard frameworks
  • Understanding of basic reliability concepts: failure rates, survival curves, mean time between failures (MTBF)
  • Prior internship or project analyzing real-world operational data: manufacturing, logistics, quality control, IoT sensor data
  • Experience with version control (git) and collaborative data analysis workflows
  • Coursework or projects in industrial engineering, operations research, or quality management
  • Familiarity with data cleaning and wrangling: handling missing values, outlier detection, data quality assessment
  • Understanding of experimental design: A/B testing, randomized controlled trials, confounding variables
  • Exposure to anomaly detection techniques: z-scores, control charts, boxplot analysis
  • Prior work with manufacturing or hardware production data (even from coursework or academic projects)
  • Familiarity with Jupyter notebooks, literate programming, and reproducible analysis practices

COMPENSATION
  • Base Salary: Denver: $75,000; Long Beach: $80,000

ADDITIONAL REQUIREMENTS
  • Work Location-Successful candidates will be located near Denver or Colorado Springs. While we observe a hybrid work environment, some work must be done on site.
  • Work environment-the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
  • Physical demands-the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.

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