Design, develop, and deploy predictive and prescriptive models across a variety of domains (e.g., customer behavior, operational efficiency, personalization).
Apply machine learning, deep learning, and statistical techniques to solve real-world business challenges.
Drive experimentation (A/B testing, multi-variate testing) and causal inference to validate hypotheses and measure impact.
Analyze large, complex datasets to extract key insights and translate them into strategic recommendations.
Communicate findings clearly and effectively to both technical and non-technical audiences, using compelling data knowledge and visualization.
Collaborate with product managers and business stakeholders to identify opportunities and frame data science solutions.
Work closely with data engineers, analysts, and software developers to build scalable, data-powered applications.
Mentor junior data scientists, supporting technical development and scientific rigor.
Contribute to the development of reusable assets, tools, and processes to increase team velocity and impact.

7+ years of professional experience in data science, statistics, and applied machine learning.
Deep proficiency in Python or R, with strong skills in libraries like scikit-learn, TensorFlow/PyTorch, models, algorithms and ontologies.
Building and deploying Data Mesh architectures.
Strong experience in AWS tools and infrastructure and cloud AI and data tools essential. Experience in working in other cloud environments (GCP, or Azure) would be an advantage.
Demonstrated success deploying models into production environments using APIs, pipelines, or ML frameworks.
Proven track record in statistical modeling, time series forecasting, NLP, or optimization.
Experience designing and analyzing controlled experiments (A/B testing, uplift modeling).
Background in quantitative disciplines such as Computer Science, Statistics, Mathematics, or Engineering.
Experience with tools such as Databricks, SageMaker, and Snowflake is essential.
Experience of Palantir would be an advantage.
AWS cloud certifications or ML specialization credentials are an advantage.

At R&P, we don’t just build software; we help our clients build the future.
We are a diverse, globally distributed team that thrives on solving challenging problems and delivering exceptional results.

Data Architect

Key skills