Data Scientist

1. Education & Certification

- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, Engineering, or related field; Master's degree is a strong plus for senior roles.

- Additional training/certifications in data science / ML / statistics (e.g., recognized DS/ML programs, cloud data/ML certifications) are advantageous.

- Bachelor's in CS/Data Science/AI/Math/Engineering (AI/ML/Cloud certifications a plus)

- Strong ML & Deep Learning fundamentals with TensorFlow/PyTorch/Scikit-learn

- Experience building GenAI/LLM solutions (Prompt Engineering, RAG, evaluation)

- Familiar with MLOps/LLMOps, CI/CD, Docker/Kubernetes, APIs, cloud AI services

- Strong problem-solving, communication & cross-team collaboration

- Maximum 35 years old

2. Knowledge & Technical Skills

- Strong foundation in statistics and machine learning (hypothesis testing, regression, classification, clustering, time-series, evaluation metrics).

- Proficiency in Python and SQL, including common DS/ML libraries (pandas, NumPy, scikit-learn; plus familiarity with at least one framework such as TensorFlow or PyTorch is a plus).

- Experience with data visualization and storytelling tools (e.g., notebooks, BI tools) to communicate findings.

- Familiarity with MLOps concepts (versioning, model registry, monitoring, retraining triggers) and ability to work within such frameworks, even if not implementing infra themselves.

- Understanding of telecom / ISP / subscription business metrics (churn, ARPU, usage, NPS, campaign metrics) is a strong advantage.

3. Soft Skills / Behavioural

- Strong analytical and critical thinking; able to distinguish correlation vs causation and challenge assumptions.

- Excellent communication and storytelling skills, capable of translating complex analysis into simple, compelling business narratives.

- Collaborative team player, comfortable working with engineers, business stakeholders, and governance functions.

- Curiosity, experimentation mindset, and resilience when facing incomplete data or changing priorities.

4. Experience

- Typically 3+ years in Data Science, Advanced Analytics, or a closely related field for a standard Data Scientist role (less for junior, more for senior/lead).

- Demonstrated experience delivering end-to-end projects: from problem framing and data exploration through modeling and presenting recommendations, ideally with measurable business impact.

- Experience in telecom, media, ISP, or other data-intensive subscription businesses is a plus.