CODA Graduate Data Scientist

Your role

We are looking for a Junior Data Scientist to join the CODA team and support the development of advanced analytics and AI capabilities for Atlas Copco’s group-wide reporting platform.

CODA brings together Sales, Finance and ESG data on Databricks to provide trusted, transparent and near real-time insights for entities, divisions, business areas and group users.

The initial focus will be on anomaly detection, with future opportunities to contribute to predictive analysis, forecasting and scenario-based insights.

Working closely with the Product Owner, business SMEs, CODA data engineers and BI developers, the successful candidate will help translate reporting and business challenges into practical, explainable and scalable analytical solutions that improve data quality, reduce manual reporting effort and support better decision-making.

This position will be supported by a part-time Senior Data Scientist in the Global IT Hub.

Responsibilities

• Analyse CODA datasets to identify unusual trends, data quality issues, reporting risks and potential business drivers.
• Collaborate with Product Owner, business SMEs, controllers, data engineers and BI developers to translate reporting challenges into practical analytical solutions.
• Build, apply and maintain statistical and machine learning models for CODA use cases, with an initial focus on anomaly detection across Sales and Finance transactional data, under the guidance and support of a Senior Data Scientist.
• Support automation of recurring analytical checks for reporting cycles such as month-end close, using Python, SQL, and Databricks workflows to improve efficiency.
• Document model logic, assumptions, limitations, controls and validation results to support trust, governance and knowledge sharing.
• Provide clear explanations of analytical outputs so they can be understood by non-technical stakeholders and used in BI reports, analytics products and future AI-enabled capabilities.
• Work with CODA data engineers to prepare data, validate inputs and support operationalisation in Databricks.
• Define and test model features, thresholds, scoring logic and validation methods under the guidance of a Senior Data Scientist to improve accuracy, explainability and reduce false positives.
• To succeed, you will need

Must-have Qualifications and Experience

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Finance, or a related quantitative field.
• 0-2 years of experience in data analytics, machine learning, or predictive modelling through industry experience, internships, academic projects, or research.
• Exposure through academic projects, internships or practical experience to forecasting techniques such as ARIMA, SARIMA, Prophet, Exponential Smoothing, XGBoost, Random Forest, LSTM, Temporal Fusion Transformers, or similar time series models.
• Proficiency in Python (Pandas, Num Py, Scikit-learn) and SQL for data analysis, data preparation, and model development.
• Understanding of statistical concepts, exploratory data analysis, feature engineering, and model evaluation techniques.
• Experience working with structured datasets and performing data cleaning, transformation, and validation.
• Familiarity with data visualization tools such as Power BI, Tableau, or similar platforms.
• Strong analytical and problem-solving skills with an ability to interpret data and generate actionable insights.
• Good communication skills with the ability to explain analytical findings to both technical and non-technical stakeholders.
• Eagerness to learn new technologies, machine learning techniques, and forecasting methodologies.
• Good-to-have Skills
• Sales, Finance or controlling domain exposure is a key advantage.
• Experience with sales or financial business big data.
• Experience of scrum agile way of working.
• Academic, internship or project experience in anomaly detection, time-series analysis, forecasting or predictive modelling.
• Familiarity with cloud platforms such as Azure, AWS, GCP or Databricks.
• Exposure to ML frameworks such as Scikit-learn, Tensor Flow, Py Torch, or similar tools.
• Understanding of data engineering fundamentals, ETL processes, and data pipelines.
• Exposure to MLOps tools and practices such as MLflow, Docker, Git, or CI/CD pipelines.
• Exposure to Gen AI and LLM-based solutions for automated insights, reporting, or data analysis.
• Knowledge of ERP systems such as SAP, Oracle, or Dynamics 365.
• Preferred Time Series Requirement
• Hands-on experience through projects, internships, or coursework in developing forecasting models and analyzing time-dependent data, including trends, seasonality, and business drivers.
• Ability to evaluate forecast accuracy using metrics such as MAE, RMSE, MAPE, or similar measures.
• In return, we offer
• Culture of trust and accountability
• Lifelong learning and career growth
• Comprehensive compensation and benefits
• Health and well-being
• Job location

You will be based from our Head Office in Hemel Hempstead or one of our other UK-based office locations, with travel to CODA hub locations, as required (Brno, Pune, Antwerp, Sweden).

• Contact information
• Talent Acquisition Team: April Harbour
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With careers sparked by initiative and lifelong learning, we unite curious minds, and you could be one of them.