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Artificial Intelligence • Information Technology • Machine Learning • Natural Language Processing • Productivity • Software • Generative AI
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Top Skills:
AISaaS
Artificial Intelligence • Information Technology • Machine Learning • Natural Language Processing • Productivity • Software • Generative AI
The role involves leading M&A strategy and investor relations, building relationships with key stakeholders, and driving deal execution and reporting.
Top Skills:
AIFinancial ModelingM&A Strategy
Artificial Intelligence • Information Technology • Machine Learning • Natural Language Processing • Productivity • Software • Generative AI
The Partner Technical Enablement Manager at Superhuman will develop technical enablement assets, launch learning paths, and lead training for partners to enhance product adoption and success.
Top Skills:
APIsCloud InfrastructureCmsEnablement ToolingLmsSaas Platforms
The Company
Outrider is a software company that is automating distribution yards with electric, self-driving trucks. Our system eliminates manual tasks that are hazardous and repetitive while it improves safety and efficiency. Outrider’s mission is to drive the rapid adoption of sustainable freight transportation. We are a private company founded in 2018 and backed by NEA, 8VC, Koch Disruptive Technologies, NVIDIA, and other top-tier investors. Our customers are Fortune 200 companies and our autonomous trucks are already running in distribution yards. For more information, visit www.outrider.ai
OverviewWe are seeking a Data Analyst with strong SQL and Python skills to build, optimize, and support Streamlit dashboards and the data systems behind them. This role focuses on delivering reliable, performant analytics products by developing dashboards, improving underlying tables and pipelines, and ensuring data freshness and accuracy.
Key Responsibilities- Build and maintain Streamlit dashboards for business reporting and operational insights.
- Optimize dashboard performance (query tuning, caching strategies, efficient data loading, and rendering).
- Design, develop, and maintain SQL tables, views, and models that feed dashboards.
- Implement and maintain Python-based data workflows to transform, validate, and deliver data for analytics use cases.
- Monitor and improve data quality, freshness, and completeness (including automated checks and alerting).
- Partner with stakeholders to define metrics, ensure consistent definitions, and translate requirements into data products.
- Maintain documentation for dashboards, datasets, KPI definitions, and data lineage.
- Troubleshoot data and dashboard issues, providing timely support and root-cause analysis.
- Strong experience with SQL (complex joins, window functions, performance optimization, and data modeling).
- Strong experience with Python for data work (e.g., pandas, data validation patterns, and APIs as needed).
- Experience building dashboards using tools such as Streamlit (ideal), Looker, Tableau, etc.
- Solid understanding of analytics engineering fundamentals: dimensional modeling, metric definitions, and reproducible transformations.
- Knowledge of basic software engineering practices (Git workflows, code reviews, testing, CI/CD)
- Professional experience building dashboards in Streamlit, including performance tuning and maintainability.
- Familiarity with modern data stack concepts (ELT patterns, semantic layers, governance).
- Experience implementing automated data quality tests and observability.
- Experience working with large datasets and improving query and pipeline efficiency.
- Dashboards are fast, stable, and trusted by stakeholders.
- Underlying tables and transformations are efficient, well-documented, and easy to maintain.
- Data quality issues are detected early, with clear ownership and resolution paths.
- Metrics are consistent across dashboards and align with agreed business definitions.
- Strong analytical thinking and attention to detail.
- Ability to balance delivery speed with data correctness and maintainability.
- Clear written and verbal communication with technical and non-technical partners.
- Comfortable owning end-to-end analytics products from requirements to production support.
Assignment:
- 4-month contract with possibilities to extend
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
