Bitdefender

Bitdefender is a cybersecurity leader delivering best-in-class threat prevention, detection, and response solutions worldwide. Guardian over millions of consumer, enterprise, and government environments, Bitdefender is one of the industry’s most trusted experts for eliminating threats, protecting privacy, digital identity and data, and enabling cyber resilience. With deep investments in research and development, Bitdefender Labs discovers hundreds of new threats each minute and validates billions of threat queries daily. The company has pioneered breakthrough innovations in antimalware, IoT security, behavioral analytics, and artificial intelligence and its technology is licensed by more than 180 of the world’s most recognized technology brands. Founded in 2001, Bitdefender has customers in 170+ countries with offices around the world. For more information, visit https://www.bitdefender.com

Job Description:

As part of the Data Science team, you will develop and deploy models by leveraging the best tools and approaches to address business needs. You will experiment with different technologies and propose the best approaches to achieve your goals, working closely with the Data Analysis, Data Engineering, and Data Warehousing teams.

Responsibilities:

Engage in the full Data Science lifecycle: data collection and preprocessing, model training, deployment, and monitoring.Conduct ad-hoc and in-depth data-driven analysis to deliver clear insights and actionable recommendations.Compare models and evaluate performance through experiment design and A/B testing.Handle multiple projects independently, ensuring well-documented processes and clear decision records.

Experience requirements:

Strong foundation in statistics (distributions, regression, hypothesis testing).Familiarity with ML techniques (e.g. boosting, ensemble methods).Strong Python programming skills.Experience in SQL operations and automating data workflows with specialized orchestration tools (e.g. Airflow).Exposure to large language model (LLM) tools and frameworks for evaluation platforms and orchestration (e.g. Haystack, LangChain, LlamaIndex), including retrieval-augmented generation (RAG) and agentic behavior.

Education:

Bachelor/Master’s degree in Computer Science, Statistics, Engineering or related fields.

Nice to have:

Distributed computing and high-performance data processing (Spark, Polars)Proficiency with advanced statistical modeling and modern ML methods, alongside a solid grasp of embedding techniques and their practical applications.Capable of translating intricate technical ideas into intuitive terms, with a clear focus on how and why they work