Job description
Method is a global design and engineering consultancy founded in 1999. We believe that innovation should be meaningful, beautiful and human. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams based in New York, Charlotte, Atlanta, London, Poland, Bengaluru, and remote work with a wide range of organizations in many industries, including Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services.
Method is part of GlobalLogic, a digital product engineering company. GlobalLogic integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. GlobalLogic is a Hitachi Group Company.
We’re seeking two hands-on Data Scientists to join our Data & AI Team. Both will be responsible for improving, optimizing, and scaling Monte Carlo–based simulation algorithms that support advanced maintenance planning and assessment. For one of these positions, experience in reliability engineering or reliability science—particularly in the context of maintenance planning and asset performance assessment—is highly desirable. Working closely with solution architects and fellow data scientists, you will analyze and optimize Python code, benchmark performance across CPU and GPU environments, and design experiments to assess feasibility at scale. Your work will span from algorithm refinement and performance testing to developing proof-of-concept architectures on Azure, creating parameterized simulations, and producing benchmark reports that inform product vision and cost/benefit trade-offs. These roles are highly collaborative, requiring both strong technical depth and the ability to translate simulation results into actionable insights for future product and service offerings.
Travel for team and client meetings is required, typically up to 15%.
Responsibilities:
- Collaborate with a cross-functional team (designer, product strategy, solution architects) to improve and optimize Monte Carlo–based simulation algorithms for predictive maintenance planning.
- Analyze, refactor, and optimize existing Python code to ensure performance, scalability, and adherence to best practices.
- Benchmark algorithm performance at component and system levels under varying data volumes and hardware configurations (CPU vs GPU).
- Design and execute experiments to evaluate the feasibility of large-scale simulation deployments on cloud environments (Azure preferred).
- Develop proof-of-concept data workflows, including parameterized simulations, scenario scaling, and distribution-based reporting.
- Contribute to defining the Minimum Viable Architecture (MVA), including cost assessment, hardware/software requirements, and integration pathways.
- Produce technical deliverables: benchmark reports, code optimization documentation, cost/performance trade-offs, and recommendations for next phases.
Qualifications:
Education
- Master’s or PhD in Computer Science, Data Science, Applied Mathematics, Operations Research, or a related field.
- 3–5+ years of professional experience in data science, computational modeling, or applied research (industry or advanced research projects).
Technical skills
- Strong proficiency in Python, including code optimization, profiling, and use of libraries for scientific computing (NumPy, SciPy, pandas, Dask, Numba, etc.).
- Experience with Monte Carlo simulation methods or other stochastic modeling techniques.
- Familiarity with high-performance computing (parallelization, GPU acceleration, CUDA, RAPIDS, or equivalent).
- Hands-on experience with cloud platforms (Azure preferred, AWS or GCP acceptable), including resource provisioning, scalability, and cost management.
- Understanding of data architecture principles and ability to work with large, complex datasets.
- Experience in building data-driven reports and visualizations (e.g., matplotlib, Plotly, Dash, or equivalent).
- Knowledge of software engineering practices (version control, testing, static analysis, code reviews).
Preferred / nice to have
- Experience with predictive maintenance, reliability engineering, or asset management solutions.
- Familiarity with APM systems and energy/utility sector solutions.
- Prior exposure to Azure Machine Learning, Databricks, or distributed computing frameworks.
- Experience integrating simulation algorithms with UI components or dashboards.
Soft skills
- Strong analytical and problem-solving skills with ability to translate complex simulations into actionable insights.
- Ability to work in a collaborative, cross-functional, and international team environment.
- Excellent communication and documentation skills for technical and non-technical stakeholders.
- Proactive, research-oriented mindset with focus on experimentation and feasibility assessment.
Why Method?
We look for individuals who are smart, kind and brave. Curious people with a natural ability to think on their feet, learn fast, and develop points-of-view for a constantly changing world find Method an exciting place to work. Our employees are excited to collaborate with dispersed and diverse teams that bring together the best in thinking and making. We champion the ability to listen, and believe that critique and dissonance lead to better outcomes. We believe everyone has the capacity to lead and look for proactive individuals who can take and give direction, lead by example, enjoy the making as much as they do the thinking, especially at senior and leadership levels.
We believe in work/life balance. Seriously. We offer a ton of competitive perks, including:
- Continuing education opportunities
- Flexible PTO and work-from-home policies
- Private medical care (can be extended to your family)
- Cafeteria system as part of the Benefit platform
- Group life insurance
- Creative TAX-deductible cost
- Other location specific perks (just ask!)
Next Steps
If Method sounds like the place for you, please submit an application. Also, let us know if you have a presence online with a portfolio, GitHub, Dribbble or other platform.
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