Data Architect

Overview

This is a high-impact role ideal for a candidate who thrives at the intersection of innovation and execution. You’ll be an integral part of a multidisciplinary team using data science and machine learning to drive intelligent products, automation, and smarter business decisions.

• Modeling & Advanced Analytics:
  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.
• Data Exploration:
  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.
• Partnership & Enablement:
  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, Tensor Flow/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, Sage Maker, and Snowflake is essential.
• Experience Of Palantir Would Be An Advantage.
• AWS cloud certifications or ML specialization credentials are an advantage.

• Scientific Thinking – Brings strong analytical and statistical foundations; frames problems rigorously and tests hypotheses systematically.
• Practical Execution – Focuses on delivering actionable insights and real-world model impact, not just theoretical accuracy.
• Business Acumen – Understands the “why” behind the data; aligns analysis with client goals and product strategy.
• Ownership & Accountability – Delivers high-quality, reliable work end-to-end; takes pride in outcomes.
• Communication – Explains technical results clearly and persuasively to diverse audiences.
• Team Collaboration – Works effectively across functions and time zones; contributes to a culture of openness and shared learning.

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. If you're a data scientist who’s excited about building intelligent systems that deliver measurable impact, we’d love to hear from you.

• Tech stack:
• Python, Tensor Fow/PyTorch, Data Mesh, AWS, Databricks, Sage Maker, Snowflake