Data Scientist

Job Description

We are looking for a motivated and analytical Data Scientist with 2–4 years of experience to join our growing data team. The ideal candidate should be comfortable working with large datasets, building predictive models and generating actionable insights. Experience or familiarity with the AEC industry is a strong plus, as the role involves working on data generated from engineering, construction and design workflows.

As a Data Scientist, you will play a key role in turning raw data into insights that support strategic decision-making. You will collaborate with cross-functional teams including software developers and domain experts. Your analytical models and tools will help enhance project performance, reduce risk and drive operational efficiency.

Key Roles and Responsibilities :

• Analyze large datasets to identify trends, patterns and actionable insights
• Design and implement machine learning and neural network models for predictions, classifications and clustering
• Collaborate with AEC domain teams to understand data requirements and propose technical solutions
• Clean, transform, and validate data using SQL, Python
• Support automation of machine learning (ML) workflows and model reporting pipelines
• Document data science processes and results for transparency and reproducibility
• Create dashboards and visualizations using tools like Power BI, Tableau or Plotly

Qualifications and Experience Required :

Compensation :

The selected candidate will receive competitive compensation and remuneration policies in line with qualifications and experience. Compensation will not be a constraint for the right candidate.

What We Offer :

Requirements

1. Analyze large datasets to extract insights and trends 2. Build ML models (prediction, classification, clustering) using Python 3. Work with AEC domain teams to define data solutions 4. Clean, transform, and validate data (SQL, Python) 5. Automate ML workflows and reporting 6. Create visualizations using Power BI, Tableau, or Plotly 7. Document processes for reproducibility