Data Scientist
Role Description
As a Data Scientist, you will turn raw data into actionable insights and predictive models that support key business decisions. You will work across the full analytics lifecycle – from data collection and exploration to model building, validation, and communication of results.
You will collaborate closely with cross-functional teams, including engineers, analysts, and business stakeholders, to identify opportunities where data can drive better decisions, and to turn analytical findings into solutions that are understood and adopted across the business.
Key Responsabilities
- Collect, clean, and explore structured and unstructured data from multiple sources.
- Design and build statistical models and machine learning algorithms to solve business problems.
- Perform exploratory data analysis to identify trends, patterns, and correlations.
- Validate model performance using appropriate statistical methods and metrics.
- Communicate findings and recommendations clearly to technical and non-technical stakeholders through reports, dashboards, and presentations.
- Collaborate with data engineers to design and maintain reliable data pipelines.
- Partner with business stakeholders to translate objectives into analytical questions and measurable outcomes.
- Support the deployment and monitoring of models in production in collaboration with engineering teams.
- Ensure data quality, integrity, and compliance with data privacy and governance standards.
- Stay current with new statistical techniques, machine learning methods, and tools, proposing their adoption when relevant.
Job Qualifications
- Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.
- [X]+ years of experience in a Data Scientist or similar analytical role.
- Strong foundation in statistics, probability, and applied mathematics.
- Proven experience building and validating machine learning models on real-world datasets.
- Ability to translate ambiguous business questions into structured analytical approaches.
- Strong communication skills, with the ability to present complex findings in a clear, actionable way.
- Strong English (written & spoken); additional languages are a plus.
- Ability to work independently and as part of a distributed/remote team.
Main Tech Skills
- Programming Languages: Python and/or R (required); SQL for data querying.
- Data Analysis & Manipulation: Pandas, NumPy, dplyr.
- Machine Learning: scikit-learn, XGBoost, LightGBM; deep learning frameworks (TensorFlow, PyTorch) a plus.
- Statistical Analysis: hypothesis testing, regression, experimental design (A/B testing).
- Data Visualization: Matplotlib, Seaborn, Plotly, Power BI, or Tableau.
- Big Data Tools: Spark, Hadoop (familiarity a plus).
- Cloud Platforms: AWS, Azure, or GCP data/ML services.
- Databases: SQL and NoSQL databases (PostgreSQL, MongoDB).
- Version Control & Collaboration: Git, Jupyter Notebooks.
- MLOps Basics: model versioning and deployment concepts (MLflow, Docker) a plus.
Be Bold · Work Smart · Change Tomorrow
We started this company with a simple observation: most organizations don’t struggle because they lack technology — they struggle because solutions are too complex, disconnected from reality, or hard to sustain over time. Data and AI can create enormous value. But only when they are designed with clear intent, solid foundations, and a realistic understanding of how organizations actually operate. We’re a young, fast-growing company with plenty of opportunities to learn, evolve, and build something meaningful together.