Data Scientist (m/f/d)
Posted Apr 3, 2026
Key Responsibilities:
-
Collect, preprocess, and analyze structured and unstructured data sets related to the construction industry using statistical methods and machine learning techniques. - Develop predictive models, algorithms, and data-driven solutions to address business challenges and enhance decision-making processes. - Collaborate with software engineers, product managers, and domain experts to integrate analytical solutions into cloud-based software platforms. - Design and implement experiments, tests, and data-driven initiatives to improve product functionalities and user experience. - Perform exploratory data analysis to identify trends, patterns, and correlations within construction-related datasets. - Communicate findings and insights to both technical and non-technical stakeholders through reports, visualizations, and presentations. - Stay updated with the latest advancements in data science, machine learning, and construction technology to drive innovation within the organization. Qualifications Required:
-
Masters degree in Computer Science, Data Science, Statistics, or a related quantitative field. - 5 years of previous experience in a Data Scientist or similar role, preferably within the software industry or construction domain. - Proficiency in programming languages like Python or R for data analysis, machine learning, and statistical modeling, with expertise in relevant libraries. - Strong understanding of machine learning techniques (supervised/unsupervised learning, regression, clustering, normalization, etc.), along with practical experience using libraries like scikit-learn, TensorFlow, PyTorch, etc. - Hands-on experience working with large datasets, utilizing data visualization tools (especially Power BI), and working with SQL/NoSQL databases. - Excellent problem-solving abilities and adeptness in translating business requirements into data-driven solutions. - Effective communication skills, capable of presenting complex findings in a clear and understandable manner. - Proficient in creating models from scratch and fine-tuning existing models. - Good understanding of Spark SQL and PySpark. - Ability to contribute to the productionization of models. - Experience in entire ML application development lifecycle - data preparation, experiment tracking, model result reproducibility, and deployment. - Experience of working with both ML Training and Inference pipelines. - Experience in using tools like ML Flow for ML development tracking, Apache Spark for deploying ML Production applications, etc. - Flexibility to work with both Traditional and Neural network based ML models for use cases spanning NLP, Computer Vision, and Tabular Structured data.