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About the Role

Collaborate with multidisciplinary individuals and support the project lead on customer experience strategy, product design, data strategy, and business strategy projects. Build data analytics and predictive & prescriptive models that support various business initiatives from personalization engines to suggestive product analytics insights and tailored marketing campaigns.

Responsibilities

Lead or co-lead data and AI engagements end to end, taking ownership of the quality of data deliverables and the structure of the work.
Combine design thinking and user-centric techniques with quantitative analysis to diagnose, define and design data-led products and services.
Facilitate technical discovery sessions and workshops with clients to uncover pain points, opportunities and available data sources.
Design and build predictive and prescriptive ML models, from personalization and recommendation engines to lead scoring, CLTV, forecasting and risk detection.
Design and prototype GenAI and LLM solutions (RAG, agentic workflows, summarisation) that are secure, compliant and grounded in real user needs.
Build BI dashboards and data products, and define the KPIs and analytics layers that track adoption and outcomes.
Develop data strategies, data-to-decision mappings and phased roadmaps that connect business objectives to technical delivery.
Present work to client stakeholders up to C-suite and board level, using a highly visual approach that engages non-technical audiences.
Write clear documentation and handovers so clients can take solutions into production.
Support sales pursuits by scoping projects, writing proposals and SOWs, and building rapid POCs that prove value and feasibility.
Champion best practice in MLOps, cloud architecture and code quality, and review the work of other Data Designers.
Mentor junior Data Designers, interns and residency members, and contribute to onboarding materials.

Requirements

4+ years in data science, ML engineering or data consulting, including leading client-facing projects.
Proven experience designing and deploying ML models that predict customer needs and behaviours, such as personalisation, recommendation, lead scoring, CLTV or churn and risk models.
Knowledge of recommendation and personalisation techniques: collaborative and content-based filtering, matrix factorisation, embeddings, contextual and UI personalisation.
Hands-on experience with LLMs and GenAI: prompt design, RAG, vector databases, and frameworks such as LangChain/LangGraph or Hugging Face.
Strong Python and SQL, with working knowledge of the wider ML stack (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost).
Experience with cloud data and ML platforms, ideally AWS (S3, SageMaker Pipelines, Glue) and/or GCP, and MLOps practices (MLflow, CI/CD, Docker, infrastructure as code).
Ability to build BI dashboards (Power BI or Tableau) and quick, visual prototypes (for example Streamlit or React).
Excellent written, verbal and visual communication, with experience presenting to senior, non-technical stakeholders.
Confident running workshops and discovery sessions and turning ambiguous problems into clear scope.
Experience in pre-sales: scoping, proposal and SOW writing, and building POCs.
Experience with agentic AI, NLP, forecasting, Monte Carlo simulation or reinforcement learning.
Data engineering exposure: Airflow, Spark, Snowflake, PostgreSQL, MongoDB.
Experience hiring and mentoring, or contributing to a growing team's tooling and ways of working.
Domain experience in one or more of healthcare, financial services, retail, automotive, energy or EdTech.
A STEM degree or equivalent practical experience.

Skills

Data Science
ML Engineering
Data Consulting
Customer Experience Strategy
Product Design
Data Strategy
Business Strategy
Data Analytics
Predictive Modeling
Prescriptive Modeling
Personalization Engines
Product Analytics
Marketing Campaigns
Design Thinking
User-Centric Techniques
Quantitative Analysis
ML Models
GenAI
LLM Solutions
RAG
Agentic Workflows
Summarisation
BI Dashboards
Data Products
MLOps
Cloud Architecture
Code Quality
Python
SQL
Pandas
Scikit-learn
PyTorch
TensorFlow
XGBoost
AWS
GCP
MLflow
CI/CD
Docker
Infrastructure as Code
Power BI
Tableau
Streamlit
React
Prompt Design
Vector Databases
LangChain
LangGraph
Hugging Face
NLP
Forecasting
Monte Carlo Simulation
Reinforcement Learning
Airflow
Spark
Snowflake
PostgreSQL
MongoDB

Location

New York
Charlotte
Atlanta
London
Tokyo
Bengaluru
Remote

Work Type

Remote First
Hybrid

Experience Level

4+ years
Senior
Leadership

Education Level

STEM degree or equivalent practical experience

Benefits

Continuing education opportunities
Flexible PTO
Work-from-home policies
Pension
Health and dental benefits
Company lunches
Company outings
Snacks
Health and wellness programs

About the Company

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.
Method is part of Hitachi Digital, a digital product engineering company.
Hitachi Digital integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses.
Hitachi Digital is a Hitachi Group Company.