Method, Inc.
Overview
Method is a global design and engineering consultancy founded in 1999. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams are based in New York, Charlotte, Atlanta, London, Poland, Bengaluru, and remote, working with a wide range of organizations in Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services. Method is part of GlobalLogic, a digital product engineering company and a Hitachi Group Company. We’re seeking two hands-on Data Scientists to join our Data & AI Team. Both roles will be responsible for improving, optimizing, and scaling Monte Carlo–based simulation algorithms that support advanced maintenance planning and assessment. For one position, experience in reliability engineering or reliability science—particularly in maintenance planning and asset performance assessment—is highly desirable. 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 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 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. 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 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. 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. Let us know if you have a portfolio, GitHub, Dribbble, or other online presence.
#J-18808-Ljbffr
Method is a global design and engineering consultancy founded in 1999. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams are based in New York, Charlotte, Atlanta, London, Poland, Bengaluru, and remote, working with a wide range of organizations in Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services. Method is part of GlobalLogic, a digital product engineering company and a Hitachi Group Company. We’re seeking two hands-on Data Scientists to join our Data & AI Team. Both roles will be responsible for improving, optimizing, and scaling Monte Carlo–based simulation algorithms that support advanced maintenance planning and assessment. For one position, experience in reliability engineering or reliability science—particularly in maintenance planning and asset performance assessment—is highly desirable. 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 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 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. 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 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. 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. Let us know if you have a portfolio, GitHub, Dribbble, or other online presence.
#J-18808-Ljbffr