EMEIA Sales Finance - Data Scientist

EMEIA Sales Finance - Data Scientist

Apple is a place where extraordinary people gather to do their best work. Together we create products and experiences people once couldn't have imagined - and now can't imagine living without. If you're motivated by the idea of making a real impact, and joining a team where we pride ourselves in being one of the most diverse and inclusive companies in the world, a career with Apple might be your dream job! You will become a member of the Sales Finance team at Apple. With approximately 150 employees based across 4 regional hubs within EMEIA (Europe, Middle East, India and Africa), we create value, collaborating with many teams to provide outstanding commercial and financial support. We set the bar high; go out of our way to help others; share knowledge; filter out noise to focus on the essential; encourage the very best from ourselves and the team; and drive the right course of action. To do all of this you will be an excellent communicator, collaborator and innovator, with a passion for debate and inclusion.

Description

Our team provides data & automation infrastructure to enable commercial insights for EMEIA Sales Finance. We superusers of analytics & BI platforms (e.g. Tableau, SAP BusinessObjects, and various internal tools), databases (e.g. Dremio, Snowflake), and data science platforms (e.g. Dataiku). We help to train others and encourage adoption of these technologies in the wider EMEIA Sales Finance team. As a Data Scientist, you will be a key driver of our machine learning forecast initiative, supporting the demand forecasting function in Sales Finance. You will be responsible for the end-to-end lifecycle of machine learning models - from ideation and data exploration to deployment and monitoring in a production environment. You will collaborate with cross-functional teams of finance analysts, project managers, and other data scientists to solve some of our most challenging problems and drive AIML adoption across Sales Finance.

Responsibilities

Problem Formulation: Collaborate with stakeholders to identify business opportunities and translate them into well-defined machine learning problems.

Data Sourcing & Preparation: Query and process large-scale, complex datasets from our key databases (Snowflake and Dremio). Design and implement efficient data pipelines for model training and feature engineering.

Model Development: Research, design, and implement machine learning models (e.g., classification, regression, forecasting, NLP) using traditional & deep learning methods.

End-to-End MLOps & Automation: Design, build, and maintain robust, automated ML pipelines for training, evaluation, and deployment. You will own the operational lifecycle of your models.

Cloud-Native Deployment: Deploy and manage scalable ML services on cloud platforms. Depending on the project, you may be responsible for the infrastructure that runs your models in production.

Experimentation & Monitoring: Rigorously test models and implement comprehensive monitoring to track performance, drift, and data quality.

Communication: Clearly communicate complex findings, model behaviours, and performance metrics to both technical and non-technical audiences.

Minimum Qualifications

5 years of hands-on experience building and deploying machine learning models in a production environment.

Strong proficiency in Python and its core data science libraries (e.g. pandas, scikit-learn, statsmodels, NumPy, PyTorch, TensorFlow, LightGBM)

Strong proficiency in SQL with hands-on experience querying and manipulating data in modern data platforms like Snowflake or Dremio.

Experience in developing and maintaining data pipelines.

Demonstrable experience with MLOps principles and tools, including workflow orchestration frameworks (e.g. Metaflow).

Experience in applying machine learning techniques to provide solutions to real business problems, including for time series forecasting.

Solid understanding of the theory behind statistical analysis and machine learning.

Experience with cloud data science platforms: Dataiku (preferred), DataRobot, Databricks, AWS SageMaker, Google Cloud AI Platform, etc.

Basic experience with deploying infrastructure on cloud platforms like AWS or GCP.

Basic knowledge and understanding of software design principles and how to apply them (SOLID, DRY, modularity, abstraction, consistency, etc.)

Experience in full data science project delivery lifecycle - from identifying the underlying business needs to delivering projects in a manner that meets those needs.

Curiosity to understand new data science tools and how they can be leveraged to meet business needs.

Ability to translate technical content for non-technical audiences and vice-versa.

Strong verbal / written communication skills.

Creativity to go beyond current tools to deliver the best solution to the problem.

Detail oriented and self-motivated individual able to function effectively when working independently or in a team.

BS/MS in Data Science/Machine Learning, Mathematics, Statistics, Information Systems, or related field.

Preferred Qualifications

Familiarity with MLOps practices is a plus.

Experience with Git is a plus.

Experience using Tableau is a plus.

Experience using BusinessObjects is a plus.