Strength. Care. Growth.
You’ll know A1 Bulgaria is the right place for you if you are driven by:
- Opportunities to learn and build your career.
- Meaningful work in a stable and fast-paced company.
- Diversity of people, projects, and platforms.
- A supportive, fun, and inspiring place to work.
Your daily routine would include:
- Collecting, exploring, and preprocessing data to ensure quality, consistency, and readiness for analysis and model development.
- Performing exploratory data analysis (EDA) to identify trends, patterns, and actionable insights.
- Developing, training, validating, and evaluating machine learning models to address business challenges.
- Engineering and selecting relevant features to improve model performance and predictive accuracy.
- Collaborating with business stakeholders to understand requirements and translate them into data-driven solutions.
- Working closely with software engineers, product owners, and other cross-functional teams to support AI/ML initiatives.
- Writing clean, maintainable, and well-documented Python code for data processing, analysis, and model development.
- Creating visualizations and reports to communicate analytical findings and model performance to both technical and non-technical audiences.
- Monitoring model performance, validating results, and supporting continuous model improvement through experimentation and evaluation.
- Using SQL to extract, manipulate, and validate data from various data sources.
- Managing code and experiments using Git, Jupyter Notebooks, and project management tools such as Jira.
- Continuously researching new AI, machine learning, and data science techniques to improve existing solutions and support innovation.
We’ll know you can make it if you have:
- 1+ year of hands-on experience in data science, machine learning, or advanced data analytics.
- Basic understanding of the Software Development Lifecycle (SDLC) and Agile/Scrum project frameworks.
- Experience in the end-to-end data pipeline, including:
- Data exploration and preprocessing (cleaning, missing value handling)
- Feature engineering and selection
- Model training, validation, and performance evaluation
- Familiarity with project management and code management tools (e.g., Jira, GitHub).
- Understanding of data science documentation and workflows:
- Jupyter Notebooks / documentation of experiments
- Model performance metrics and benchmarks
- Data dictionaries and schemas
- Ability to analyze business requirements and translate them into data-driven solutions or predictive models.
- Strong proficiency in Python and core data science libraries (e.g., Pandas, NumPy, Scikit-learn).
- Solid understanding of databases and SQL basics for data extraction, manipulation, and validation.
- Familiarity with exploratory data analysis (EDA) and data visualization tools (e.g., Matplotlib, Seaborn, Tableau, or PowerBI).
- Basic knowledge of version control systems (specifically Git).
- Fundamental understanding of core ML concepts (supervised vs. unsupervised learning, regression, classification, clustering).
- Strong analytical thinking, mathematical intuition, and a high attention to detail regarding data quality.
- Good problem-solving skills and the ability to challenge assumptions about data and model behavior.
- Ability to work both independently and collaboratively with engineers and business stakeholders.
- Good written and verbal communication skills in English (specifically the ability to explain technical insights to non-technical teams).
- Proactive attitude and a strong willingness to stay updated on rapidly evolving AI/ML trends.
Nice to have:
- Exposure to modern Generative AI techniques, Large Language Models (LLMs), or prompt engineering.
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
- Familiarity with processing large-scale datasets using cloud environments or distributed computing (e.g., basic Spark/Databricks).
- Basic knowledge of ML deployment concepts or MLOps pipelines (e.g., tracking experiments with MLflow, Docker basics).
Apply now!
