Strolll
Senior Data Scientist
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About the Role
As a Senior Data Scientist – Data & AI at Strolll, you will play a crucial role in understanding our Strolll Augmented Reality (AR) software solution application derived data including assessment and game metrics, device telemetry data. Your mission is to realise cutting-edge data that will make Strolll the most used rehabilitation software in the world.
You will be an integral part of our Data & AI team, working closely with our immediate team of engineers focused on health algorithms and assessments, as well as the broader AR platform engineering, design, and Quality Assurance, clinical, and science teams.
Our AR solution is multi-user and requires a physical space, therefore play-tests and feature evaluation requires a controlled physical environment. This role can be hybrid with a portion of your time working onsite in our dedicated office space, with the remainder from home, or fully remote for qualified candidates. You will need to be able to travel to the company HQ in Stafford, UK regularly.
This role reports to the VP – Data & AI.
Key Responsibilities
- Advanced Data Analytics Expertise: Leverage deep experience with an analytics stack — such as Azure Synapse Analytics, Azure Databricks, Azure Data Lake, and Azure Machine Learning — to design and implement scalable, secure, and cost-effective analytical solutions. Drive insight generation and decision-making through cloud-native data science workflows.
- Fluency in Python, SQL & Spark for Analytics: Expert in Python, SQL, and Spark with a strong foundation in statistical modelling, data wrangling, and algorithmic thinking. Apply these tools to build reproducible analytics pipelines, conduct exploratory data analysis (EDA), and develop predictive models that inform clinical, scientific, and operational decisions.
- Insight-Driven Problem Solver: Thrive on solving complex analytical challenges — from hypothesis generation and data exploration to statistical inference and model deployment. Architect robust solutions that support clinical research, operational intelligence, and product innovation.
- Applied Statistical Modelling & Experimentation: Design and execute rigorous statistical analyses, A/B tests, and causal inference studies. Translate complex data into actionable insights using techniques such as regression, classification, time series forecasting, and Bayesian modelling.
- Build Analytical Data Products: Deliver high-quality, production-grade analytical outputs — including dashboards, curated datasets, and model-driven insights — that power clinical decision-making, product development, and strategic planning. Ensure outputs are interpretable, reproducible, and aligned with stakeholder needs.
- Version Control & Collaborative Development: Proficient in Git and Azure DevOps for collaborative analytics development. Comfortable with peer review workflows, CI/CD pipelines, and automated testing of analytical code using tools like pytest, Great Expectations, and dbt.
- Ensure Data Integrity & Analytical Quality: Strong skills in debugging data issues and validating analytical outputs. Use data observability tools and statistical diagnostics to ensure accuracy, reliability, and reproducibility across the analytics lifecycle.
- Optimize for Performance & Impact: Analyze and optimize analytical workflows for efficiency, scalability, and stakeholder impact. Tune Spark jobs, optimize SQL queries, and streamline model training and inference in Azure environments.
- Cross-Functional Collaboration: Partner with clinical, scientific, product, and engineering teams to understand domain-specific challenges and translate them into data science solutions. Collaborate with ML engineers and data engineers to ensure data readiness and model deployment. Contribute to patents and publications where applicable.
- Growth & Thought Leadership: Continuously expand expertise in data science, cloud-native analytics, and responsible AI. Share knowledge through mentorship, internal talks, and contributions to technical reviews and research efforts.
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Qualifications & Skills
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Required:
- MS or higher in Computer Science, Data Science or commensurate training and experience.
- Solid understanding of core programming and data engineering principles, including data types, data structures, algorithms, distributed systems, and cloud-native data architecture. Familiarity with machine/deep learning pipelines, wearable sensing data, and healthcare SaaS platforms — ideally within neurorehabilitation or adjacent clinical domains.
- 5+ years of experience in Data Science roles, with a strong track record of building and maintaining production-grade data pipelines, services, and platforms — preferably using SQL, R or Python and Azure technologies such as Azure Data Factory, Azure Synapse, Azure Databricks, and Azure Event Hubs.
- Experience building, deploying, and supporting commercial-grade data analytics — including curated datasets, real-time data services, and analytics platforms — targeted at clinical assessments, scientific research, and patient-facing applications, across the full product lifecycle.
Desirable:
- Prior experience with streaming data architectures and real-time analytics using Azure stream Analytics, Kafka, or similar technologies.
- Experience working with clinically deployed applications, clinical research datasets, and regulatory-compliant data workflows (e.g., HIPAA, GDPR).
- Familiarity with data-centric AI, feature engineering, and ML model deployment in Azure ML or Databricks environments.
- Familiarity with deployment of SaaS tools in clinical health and rehabilitation or related fields.
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