Data Scientist
WHO WE ARE
We are Edge Solutions Lab, a team of engineers with a strong background in product development of edge solutions and data platforms. Our legacy is grounded in the successful execution of the Hivecell product. We're committed to helping companies implement their product strategies and turn data into real, measurable value.
WHO WE ARE LOOKING FOR
We are seeking an experienced Data Scientist to join an exciting project for our client, Flow — a forward-thinking company transforming the way people live, work, and connect. You will work on real-world data challenges at the intersection of edge computing and data platforms, turning complex, large-scale data into models and insights that directly shape Flow's digital ecosystem and drive measurable product value.
KEY RESPONSIBILITIES
- Design, develop, and deploy machine learning models and statistical solutions that address real business problems
- Collaborate with product, engineering, and domain teams to translate requirements into data-driven solutions
- Conduct exploratory data analysis and feature engineering on large, complex datasets
- Build and maintain scalable data pipelines to support model training, evaluation, and inference
- Define and track model performance metrics; monitor models in production and drive continuous improvement
- Communicate findings, methodologies, and results clearly to both technical and non-technical stakeholders
Contribute to the team's data science best practices, tooling, and documentation
WHAT YOU BRING ALONG
- 4+ years of professional experience as a Data Scientist or in a similar role
- Strong proficiency in Python and core data science libraries (pandas, NumPy, scikit-learn, etc.)
- Solid understanding of machine learning algorithms, statistical modeling, and model evaluation techniques
- Experience with deep learning frameworks (TensorFlow or PyTorch) is expected at this level
- Hands-on experience deploying models to production environments (REST APIs, containerization, cloud platforms)
- Proficiency with SQL and working with relational or distributed data stores
- Familiarity with MLOps practices: experiment tracking, model versioning, monitoring (e.g., MLflow, Weights & Biases)
- Experience working with cloud platforms such as AWS, GCP, or Azure
- Practical experience leveraging AI-powered tools (e.g., GitHub Copilot, ChatGPT, or similar) to enhance productivity and accelerate development workflows
- Working knowledge of: graph databases (Memgraph, Neo4j, or similar); edge inference runtimes (ONNX Runtime, llama.cpp, ExecuTorch); gradient boosting frameworks (XGBoost, LightGBM, CatBoost); time-series analysis on IoT or sensor data streams; Snowflake and dbt-based analytics pipelines
- English proficiency at B2 level or higher — able to communicate effectively in a professional setting, both written and spoken
WILL BE A PLUS
- Experience with edge computing or IoT data streams
- Knowledge of time-series analysis and anomaly detection
- Familiarity with data orchestration tools (Airflow, Prefect, or similar)
- Contributions to open-source projects or published research
- Experience in a product-focused or startup environment
SOFT SKILLS
- Strong analytical thinking with a pragmatic, solution-oriented mindset
- Ability to work independently and take ownership of tasks end-to-end
- Clear and concise communication — able to explain complex concepts to non-technical audiences
- Collaborative attitude and comfort working in cross-functional teams
- Intellectual curiosity and a continuous drive to learn and improve
- Comfort with ambiguity and the ability to prioritize effectively in a fast-paced environment