Machine Learning Engineer
About the job
- Three to eight years of experience.
- Bachelor's or Master's degree in Engineering, Statistics, or Mathematics.
- Hands-on expertise in machine learning models using R and Python, SQL, and statistical methodology, including statistical data analysis.
- Ability to set up environments in AWS and integrate solution components.
- Experience using Google and Amazon natural language processing APIs to parse data and analyze output.
- Experience using Amazon Docker tools to develop and deploy solutions.
- Proficiency in Java and Python programming.
- Understanding of topic modeling and supervised and unsupervised machine learning.
- Excellent hands-on working knowledge of R, Python, advanced predictive modeling, SQL, and AWS.
- Work with large, complex data sets and solve difficult, non-routine analysis problems using advanced analytical methods as needed.
- Conduct end-to-end analysis, including data gathering from various data storage platforms, requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Develop data pipelines to integrate different data sources.
- Plan project milestones, resourcing, and work distribution.
- Execute projects in a timely manner, analyze risks, and mitigate them.
- Lead a technical team of data scientists and engineers.
- Communicate project progress, challenges, results, and next action items to stakeholders.
About the company
Factspan is a data and AI company that helps global enterprises optimize business outcomes through end-to-end data and AI services. It covers the full lifecycle from strategic visioning and data strategy to engineering, analytics, AI model development, deployment, and governance, delivering pragmatic solutions across industries. Unlike many vendors, Factspan emphasizes a customer-centric approach and has a track record of partnering with Fortune 500 companies, applying cross‑industry innovations to real business problems. The company’s goal is to turn data-driven insights into tangible improvements in performance, efficiency, and decision making for large organizations.
Company Size
201-500
Company Stage
N/A
Total Funding
N/A
Headquarters
Seattle, Washington
Founded
2012
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Simplify's Take
What believers are saying
- Factspan launched Beyond the Dashboard in January 2026, expanding demand-generation and credibility.
- Nagarajan V joined as CHRO in April 2026, signaling organizational scaling.
- DDAC 2026 sponsorship and Booth #306 show active enterprise sales investment.
What critics are saying
- Factspan still lacks visible venture funding, limiting scale against better-capitalized consultancies.
- LinkedIn and website marketing dominate evidence, so customer concentration remains opaque.
- A services-heavy model faces margin pressure if GenAI implementation becomes commoditized by 2027.
What makes Factspan unique
- Factspan packages data engineering, AI, governance, and MLOps under one delivery umbrella.
- Its January 2026 GenAI QA accelerator shows practical automation, not just advisory slides.
- The 2026 case-study library signals repeatable enterprise delivery across healthcare, retail, and finance.
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