Requirements

Expected technologies

SQL

Git

Jira

Confluence

Optional technologies

AWS

Google Cloud Platform

Our requirements

• Minimum 5 years of proven industry experience in data science or applied machine learning.
• Solid business acumen, strong customer centric mindset and strategic thinker who can balance big picture strategy with tactical needs of day-day execution.
• Strong programming skills in Python.
• Expert knowledge of supervised and unsupervised machine learning methods.
• Hands-on experience with ML libraries and frameworks such as Scikit-Learn, TensorFlow, PyTorch.
• Proficient with big data tools (e.g., Spark, Hadoop) and orchestration tools.
• Solid foundation in data analytics, data visualization, data mining, statistics, and probability theory.
• Experience with SQL and relational databases.
• Familiar with agile methodologies and software development practices (Git, JIRA, Confluence).
• Experience working in cloud environments (Data Robot, AWS and GCP are a plus).
• Understanding of big data ecosystems and ETL processes.
• Previous experience working with telecom datasets (e.g., network KPIs, customer behavior) and stakeholder management skills.
• Strong problem-solving skills and a proactive, detail-oriented mindset.
• Excellent communication skills with the ability to present findings to diverse audiences.
• Fluent in English (written and spoken).

Your responsibilities

• Ensure key business drivers are captured during the design of Machine Learning solutions in collaboration with product stakeholders.
• Lead data science projects and mentor data scientists, establish standards for code quality, experimentation, documentation, and reproducibility.
• End-to-end ownership of ML models on Data Science platform, from data ingestion and feature engineering to training, deployment, monitoring, and retraining.
• Drive data strategy and governance: data contracts, quality, privacy, security, and compliance across the model lifecycle.
• Ensure reliability of models in production (SLOs/SLAs, observability, retraining schedules, alerts).
• Extract and analyze network and business data to identify trends, detect anomalies, and recommend improvements in service performance and customer experience.
• Apply advanced analytical techniques and develop predictive models and algorithms that support telecom operations across the value chain.
• Collaborate with cross-functional teams including engineering, product, and operations.
• Maintain and enhance the model development environment including pipelines and orchestration frameworks.
• Visualize and explain the outcomes of the work in a way that is understandable for diverse audiences