Job VC

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