You will:

• Advance our supply chain modernization efforts by working closely with other Data, Analytics and Automation professionals and supply chain domain experts to understand complex business problems and co-create digital solutions driven by data science models


• See your algorithms and solutions get scaled and deployed, while learning from your mistakes


• Join and contribute to a community of data scientists by sharing industry best practices and   lessons learnt from past and ongoing projects


• Identify opportunities to innovate across the supply chain through team challenges, to collaborations with external industry disruptors


• Craft and execute a personalized development plan with your manager to ensure you will continuously grow in your career aspirations, whether it be technical leadership or people leadership

We seek data scientists with a passion to…

• Deep dive into data to find answers to yet unknown questions and have a natural desire to go beneath the surface of a problem


• Ask and answer questions in large datasets and have a strong desire to create strategies and solutions that challenge and expand the thinking of everyone around you


• Collaborate in cross functional teams to co-create solutions to business problems, bringing a combination of data science expertise with business acumen to drive pragmatic, business driven value.


• Keeping abreast of latest developments in the field and identifies opportunities to apply within our organization


• Create new intellect and authoring White Papers in collaboration with academic peers and others on innovative techniques or new approaches to Machine Learning / Predictive Modelling on large-scale data


• Be a role model to junior data scientists


• Train/guide data science enthusiasts across the organization

Essential Requirements

• Bachelor's / Master’s / PhD degree in a relevant field such as Engineering, Statistics, Computer Science, Data analytics or Data Sciences with 8 – 12+ years of experience


• Relevant work experience applying data science/machine learning/statistical techniques to quantitative problems in a business environment, demonstrating ability to experiment, develop and deploy solutions into production environments.

• Experience working with cross-functional (i.e. data engineering, software development, business intelligence) digital solutioning teams integrating data science models into end-to-end solutions.


• High level of expertise in building supervised/unsupervised solutions leveraging but not limited to regression, classification, clustering, time series forecasting & NLP techniques.


• Strong foundation in carrying out open-ended data mining and analysis for medium to large datasets from disparate sources.


• Ability to efficiently query data from structured (SQL, PostgreSQL etc.) and unstructured (MongoDB, Hadoop) data stores.


• High proficiency in advanced scripting techniques including R and/or python as the primary programming language


• Advanced coding skills and ability to optimize code for performance, scalability and readability.


• Hands on experience implementing deep learning techniques such as using Keras, Pytorch, or TensorFlow.


• Foundational understanding of data science model deployment techniques in containerized environments like Kubernetes.


• Self-driven, able to work independently yet act as a team player


• Strong communication skills with the ability to flex style to suit the audience, from executives, to technical leaders, to other DAA competency specialists, or whilst coaching and guiding other team members

Highly desirable skills:

• Experience in simulation/optimization/operations research.


• Experience working in distributed computing environments like Spark (PySpark and/or SparkR).  


• Hands on experience with docker and other relevant CICD tools desirable but not required.

Our team’s way of working

• We are uncovering ways to impact every key decision in Dell’s supply chain with a data science solution.


• We use our curiosity to find latent opportunities by experimenting together.


• We work in an agile methodology and  have the autonomy to suggest and work on optimal approaches.


• We target decisions that achieve our company objectives. Our solutions address important prevailing business problems and we continuously keep this in mind while progressing through our projects.


• We excel when we collaborate. Data scientists are encouraged to seek feedback from others and have open discussions while progressing through the data science lifecycle.