Data Scientist enhancing merchandising and inventory performance using machine learning techniques at Nordstrom. Collaborating with teams to develop data - driven solutions for better customer experiences.
About the role
- Senior Data Scientist building machine learning models and analytics solutions for connected car services. Collaborating with insurance partners on driver safety insights from large datasets.
Responsibilities
- Develop, train, and deploy machine learning models for risk scoring, behavioural analytics, fraud detection and extreme event detection.
- Optimise feature engineering, model performance, and real-time inference pipelines for large-scale datasets.
- Work on supervised, unsupervised, and reinforcement learning models to enhance decision-making.
- Leverage telematics, mobility, and insurance data to generate actionable insights and product improvements.
- Conduct exploratory data analysis (EDA) to uncover trends, anomalies, and business opportunities.
- Ensure robustness and scalability of data science pipelines, minimising bias and improving accuracy.
- Work with big data processing frameworks (Spark, AWS, Azure) to scale data pipelines.
- Ensure efficient data wrangling, transformation, and feature selection using Python, SQL, and distributed computing.
- Optimise data workflows and cloud-based machine learning architectures, ensuring efficiency and performance.
- Directly work with customers and partners.
- Prepare and deliver presentations, translating data science capabilities into real-world applications.
- Collaborate with Software Engineers to deploy models via APIs, microservices, or cloud environments.
- Collaborate with the wider Engineering team to integrate machine learning models into production-grade systems.
- Stay ahead of emerging AI, ML, and data science trends, integrating innovative techniques into IMS solutions.
Requirements
- 5+ years of experience in data science, machine learning, or AI model development.
- Expertise in Python, R, or Julia, with proficiency in pandas, NumPy, SciPy, scikit-learn, TensorFlow, or PyTorch.
- Experience with SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.).
- Strong background in statistical modelling, probability theory, and mathematical optimisation.
- Experience deploying machine learning models to production (MLOps, Docker, Kubernetes, etc.).
- Familiarity with AWS/GCP/Azure cloud ML platforms for scalable model training and inference.
- Strong problem-solving, communication, and business acumen skills.
Benefits
- Flexible remote working options.
- Flexible holiday scheme (unlimited vacation) to really make the most of your time and wellbeing.
- 'Work From Anywhere' Policy - work almost anywhere in the world for 30 days per year!
- Employee Assistance Program and an enhanced maternity/paternity package.
- Funded training opportunities.
- Auto-Enrolment Pension & Private Medical Insurance.
- Cycle to Work and Car Maintenance Salary Sacrifice discounts!
- Kudos Hub - a peer-to-peer recognition system, where you can recognise others using points.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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