Senior Data Scientist

At Global IT Hub, we are more than a technology organization. We are a community of innovators, problem-solvers, and digital pioneers dedicated to accelerating Atlas Copco Group’s business potential through world-class technology solutions. Join us to shape the future of digital transformation, work on global challenges, and create impact for businesses and customers worldwide.

CLASSICAL MACHINE LEARNING  |  JOB DESCRIPTION

Role at a glance Key responsibilities

• Partner with stakeholders to frame ambiguous business needs as well-defined machine learning problems, including decision context, baselines, constraints, risks and measurable success criteria.
• Lead end-to-end delivery using CRISP-DM: business understanding, data understanding, data preparation, modeling, evaluation and deployment, with iteration and traceability across phases.
• Design, train and compare classical supervised and unsupervised learning models; select the simplest approach that meets performance, interpretability, latency, scalability and cost requirements.
• Own feature engineering, data leakage prevention, sampling strategy, cross-validation, hyperparameter optimization, threshold selection, calibration and robust error analysis.
• Build reliable evaluation frameworks using fit-for-purpose metrics, statistical tests, confidence intervals, business-cost functions and champion-challenger comparisons.
• Take models from experimentation to production with engineering teams, covering packaging, APIs or batch scoring, versioning, CI/CD, monitoring, retraining and rollback.
• Own post-production performance, including drift detection, data-quality controls, incident analysis, model refresh decisions and communication of model limitations.
• Apply explainability and responsible AI practices through model cards, reproducibility, bias and fairness checks, audit trails, privacy-aware design and human oversight.
• Set technical standards, conduct design and code reviews, mentor data scientists, create reusable components and influence the classical ML roadmap.
• Communicate recommendations, uncertainty, assumptions and trade-offs clearly to technical teams, business leaders and governance forums.

Mandatory Technical Skills

• Advanced Python and SQL, with strong hands-on use of NumPy, Pandas, scikit-learn, SciPy and statsmodels; ability to write modular, tested and maintainable code.
• Deep knowledge of regression and classification: linear and logistic regression, regularization, decision trees, Random Forest, Extra Trees, XGBoost, LightGBM, CatBoost, support vector machines, k-nearest neighbors, Naive Bayes and appropriate ensemble methods.
• Strong understanding of unsupervised learning: k-means and hierarchical clustering, DBSCAN, PCA and other dimensionality-reduction methods, anomaly or outlier detection and segmentation evaluation.
• Practical knowledge of forecasting and statistical modeling, including ARIMA/SARIMA, exponential smoothing, trend and seasonality, lag features, back-testing and prediction intervals.
• Expertise in feature selection and engineering, missing-value treatment, categorical encoding, class imbalance, resampling, pipeline design and prevention of target leakage.
• Rigorous evaluation skills across ROC-AUC, PR-AUC, precision, recall, F1, log loss, RMSE, MAE, MAPE/WAPE, lift, gain, calibration and business-impact metrics, selected according to the use case.
• Hands-on optimization using grid, random or Bayesian search, with sound cross-validation design for grouped, temporal and imbalanced datasets.
• Practical MLOps experience with experiment tracking, model registry, source control, automated testing, Docker, CI/CD, deployment and model or data monitoring.
• Working knowledge of explainability techniques such as feature importance, permutation importance, partial dependence and SHAP, including their limitations.
• Experience handling large structured datasets and collaborating on scalable data pipelines in a cloud or distributed environment.

Senior-level capabilities

• Independently owns technical direction and delivery for complex ML use cases from discovery through production adoption.
• Challenges weak problem formulations and prevents unnecessary use of ML when rules, analytics or simpler statistical methods are more suitable.
• Makes defensible trade-offs among accuracy, interpretability, maintainability, latency and cost.
• Reviews experimental design and model evidence before approving production release.
• Mentors team members and raises standards for coding, documentation, reproducibility and stakeholder communication.

Good-to-have experience

• Domain exposure in manufacturing, industrial operations, supply chain, service, pricing, finance, sales or customer analytics.
• Optimization, causal inference, survival analysis, uplift modeling, recommendation systems, geospatial analytics or graph-based techniques.
• Azure Machine Learning, Databricks, Microsoft Fabric, MLflow, Spark and enterprise data sources such as SAP.
• Experience integrating classical ML models into applications, APIs, decision-support workflows or Power BI solutions.
• Awareness of deep learning and generative AI, with the judgment to benchmark them against classical ML rather than defaulting to higher-complexity approaches.

Core algorithm and method coverage

Candidates are expected to demonstrate depth in several categories and sound model-selection judgment across the full landscape. This is not a requirement to have used every library or algorithm in production.

What you can expect from us

• Meaningful global business problems and access to cross-functional expertise.
• Freedom to choose fit-for-purpose methods and challenge unnecessary complexity.
• A collaborative environment focused on engineering quality, responsible AI and measurable value.
• Opportunities to mentor, build reusable capabilities and shape the enterprise AI practice.

 

At Global IT Hub, we believe exceptional talent drives exceptional transformation. As a strategic partner for next-generation digital transformation, we empower our people to innovate boldly, collaborate globally, and turn ideas into solutions that create lasting business value. Join a team where learning never stops, innovation is part of our DNA, and your contribution helps shape the future of a global industry leader.

 

 

Role Evolution – Next 3–4 Years

• Evolve from predictive modelling toward decision intelligence, combining prediction, causal analysis, simulation and optimization.
• Build hybrid AI solutions integrating classical ML with Generative AI, foundation models and AI agents.
• Establish systematic benchmarking across classical ML, deep learning and emerging AI models to select the best approach based on performance, explainability, scalability and cost.
• Drive automation of the end-to-end ML lifecycle, including feature engineering, experimentation, deployment, monitoring, drift detection, retraining and model retirement.
• Develop advanced model evaluation frameworks covering technical performance, business impact, robustness, explainability, fairness and operational reliability.
• Advance model observability and continuous learning, proactively managing data drift, concept drift and model degradation.
• Strengthen Responsible AI and model governance, including lineage, reproducibility, explainability, bias assessment, human oversight and auditability.
• Develop reusable ML frameworks, components and engineering standards to accelerate enterprise-wide AI development.