Hybrid (3 days in office/2 days remote) - New York, NY or Century City , CA What You'll Do: * Own ... Communicate trade-offs and impact to execs and non-technical partners; make the complex ...
Hybrid (3 days in office/2 days remote) - New York, NY or Century City , CA What You'll Do: * Own ... Communicate trade-offs and impact to execs and non-technical partners; make the complex ...
Hybrid (3 days in office/2 days remote) - New York, NY or Century City , CA What You'll Do * Own ... Communicate trade-offs and impact to execs and non-technical partners; make the complex ...
Hybrid (3 days in office/2 days remote) - New York, NY or Century City , CA What You'll Do * Own ... Communicate trade-offs and impact to execs and non-technical partners; make the complex ...
Remote Python Trading information
See Compton, CA salary details
$13.43 - $20.17
1% of jobs
$20.17 - $26.92
0% of jobs
$26.92 - $33.67
2% of jobs
$33.67 - $40.42
5% of jobs
$40.42 - $47.16
11% of jobs
$49.15 is the 25th percentile. Wages below this are outliers.
$47.16 - $53.91
18% of jobs
The median wage is $57.43 / hr.
$53.91 - $60.66
24% of jobs
$65.82 is the 75th percentile. Wages above this are outliers.
$60.66 - $67.40
18% of jobs
$67.40 - $74.15
13% of jobs
$74.15 - $80.90
5% of jobs
$80.90 - $87.65
3% of jobs
$13
$59
$87
How much do remote python trading jobs pay per hour?
What are the key skills and qualifications needed to thrive as a Remote Python Trading professional, and why are they important?
What are some common challenges faced by remote Python trading developers, and how can they be addressed?
What is a Remote Python Trading job?
What is the difference between Remote Python Trading vs Remote Quantitative Analyst?
| Aspect | Remote Python Trading | Remote Quantitative Analyst |
|---|---|---|
| Required Credentials | Python programming, finance knowledge, data analysis skills | Mathematics, statistics, programming, finance or economics degree |
| Work Environment | Financial firms, hedge funds, trading companies | Financial institutions, investment firms, research organizations |
| Industry Usage | High in trading and algorithm development | High in risk modeling and quantitative research |
| Common Search/Comparison | Yes | Yes |
Remote Python Trading focuses on developing trading algorithms using Python, primarily in trading firms. Remote Quantitative Analysts work on financial modeling and risk analysis, often requiring similar skills but with a broader focus on quantitative research. Both roles involve programming and finance, but their core responsibilities differ in application and industry emphasis.
Other
Medical, Dental, Vision, Retirement
Posted 18 days ago
StubHub rating
7.0
Based on 5 frontline employees who took The Breakroom Quiz
2nd of 3 rated ticket sellers
Job description
The Role:
We're seeking a Data Scientist 2 to lead the science behind a best-in-class ticketing experience for fans, with a focus on the operational journey from ticket purchase through event entry. In this role, you will own and develop predictive and decisioning models that proactively identify and mitigate fulfillment issues, strengthen inventory validation, and optimize customer service policies - all while balancing fan experience with operational constraints.
You will work on problems where getting it right matters: predicting issues before they impact fans, identifying emerging pain points at scale, and turning moments of potential failure into opportunities to build trust and loyalty. This includes building and productionizing models on both structured and unstructured data, such as LLM-backed pipelines that extract actionable signals from customer communications and support interactions.
You will partner closely with product managers, analytics engineers, operations leaders, and other data scientists to translate ambiguous business problems into durable, production-ready data products, and to influence decisions that measurably improve the fan experience.
Location: Hybrid (3 days in office/2 days remote) - New York, NY or Century City, CA
What You'll Do:
- Own the science behind ticket fulfillment: Drive reliability, ease of use, and customer satisfaction by crafting and deploying scalable, production-grade machine learning models that directly inform operational systems and decisions.
- Drive data-informed strategy: Work with operations partners to shape roadmaps, using analysis and modeling to identify, prioritize, and size high-impact opportunities that can be implemented and sustained at scale.
- Design for validation: Apply principles of experimentation and causal inference to ensure work products can be rigorously evaluated, defining offline validation and online testing strategies early in the design process.
- Build the data foundation: Partner with platform and analytics teams to ensure data availability and quality, including event instrumentation, batch and streaming ETL, feature stores, and monitoring for model performance, bias, and drift.
- Tell the story: Communicate trade-offs and impact to execs and non-technical partners; make the complex understandable and actionable.
What You've Done:
- 2-4 years of industry experience in data science or machine learning, with an MS or PhD in mathematics, statistics, computer science, or a related quantitative field preferred.
- Strong programming skills in Python, with experience using numerical and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, and gradient-boosting frameworks (e.g., LightGBM, XGBoost).
- Demonstrated experience building and iterating on machine learning models, including rigorous model validation and evaluation
- Expertise working with large-scale data using modern analytics and compute platforms such as Snowflake, BigQuery, or Databricks, with strong proficiency in SQL.
- Excellent communication skills, with the ability to clearly articulate modeling assumptions, tradeoffs, and impact to both technical and non-technical audiences.
Nice to have:
- Experience applying machine learning to operational problem spaces such as ticket fulfillment, fraud detection, customer service, or trust and safety.
- Experience with production ML platforms and workflows, such as Metaflow or comparable systems (e.g., Airflow-based ML pipelines, Kubeflow, SageMaker Pipelines, Prefect, Flyte), including model versioning, scheduling, and monitoring.
- Experience selecting and applying appropriate causal inference approaches to evaluate model and policy impact, including methods such as uplift modeling, synthetic control, and BSTS.
What We Offer:
- Accelerated Growth Environment: An environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale.
- Top Tier Compensation Package: Competitive base, equity, and upside that tracks with your impact.
- Flexible Time Off: Enjoy unlimited Flex Time Off, giving you the flexibility to manage your schedule and take time to recharge as needed.
- Comprehensive Benefits Package: Prioritize your well-being with a comprehensive benefits package, featuring 401k, and premium Health, Vision, and Dental Insurance options.