B2B

Data Science

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What is Xenoss

Xenoss is an innovative AI Lab dedicated to delivering advanced AI solutions and services to enterprise clients from the MarTech sector. We tackle a broad spectrum of AI-related challenges, helping businesses with problems like user behavior prediction, content generation, NLP, audience segmentation, building, and fine-tuning LLMs, crafting complex data pipelines, etc. We work with Activision Blizzard, Toshiba, Verve Group, Voodoo Games, Telefonica, and other enterprises.

We’re included in the top 100 software companies on the Inc. 5000 list.

What you will do

We are looking for an experienced Data Scientist with a broad outlook in the modern AI realm, a proven track record in end-to-end ML solutions development, and a passion for the domain.

You will be involved in one or more areas:

Client ML projects: Work closely with enterprise clients to understand their unique needs and leverage AI to solve them. It includes problems like semantic analysis of natural texts, CAC minimization for global ads campaigns, UGC sentiment analysis, optimal budget distribution for global marketing teams, building RAG pipelines to extend LLM contexts, and others.

Contribution to Xenoss AI/ML initiatives: Work with our AI group, focusing on building our ML-related toolset and developing the AI/ML community by running webinars, publishing case studies, and writing articles.
Research: Stay up-to-date with the latest in AI/ML advancements and contribute to the continuous evolution of our AI capabilities.

What should you bring

Experience: Proven track record in end-to-end machine learning model development, from data gathering and preprocessing to design ML pipelines, model training, fine-tuning, and deployment.
Education: Master’s degree in Data Science, Machine Learning, Statistics, Computer Science, or a related field (PhD preferred).
Technical skills:
Experience with cloud services (AWS, Azure, etc.) and ML deployment tools.
Proficiency in Python, specifically with the classical DS stack (NumPy, SciPy, Pandas, matplotlib, scikit-learn, etc.).
Familiarity with statistical methods and techniques, including A/B testing.
Familiarity with main Deep Learning tools and frameworks (TensorFlow/Keras, PyTorch).
Familiarity with LLMs and their application in business settings.
Experience with SQL, NoSQL, and Vector DB’s.
Upper-Intermediate level of English (written and spoken).
Analytical mindset: Strong problem-solving skills and ability to analyze complex datasets and derive meaningful insights.
Communication Skills: Ability to translate technical insights into business language for non-technical stakeholders.