Senior Data Scientist with English

Our partner is looking for a Data Scientist to boost AI/ML adoption in Commercial Organization processes & activities. Our ideal candidate will shape company’s future of how to solve concrete business problems with the use of data science, by joining a very dynamic, highly skilled and diverse global team who is pushing the innovation frontier in the way we use AI& automation solutions to solve our stakeholders’ challenges. Working with us you will combine your modelling skills, business thinking, experimentation and optimization methods to build viable solutions for company’s Commercial Organization.

The AI&Automation team is responsible for coordinating all efforts implied to increase AI&Automation adoption across company’s Commercial Organization, providing RPA/AI/ML solutions to business use cases. The key areas in scope for the team are:

• Funnel management – coordinating the development of solutions from idea submission to moving to production, according to the necessary technology and architecture required (RPA/AI/ML)
• Community – upskilling people into Citizen Developers/ Data scientists who can build solutions themselves for team& personal productivity and boosting volunteering and contribution for these upskilled individuals

If you are customer centric experienced professional creating value for business groups by developing viable and sustainable solutions using AI/ML, then you are the perfect candidate.

You will work closely with cross-functional teams to identify opportunities, match with both traditional ML methods (forecasting algorithms, recommender systems) and Generative AI and large language models. and deploy solutions in real-world applications. Your expertise in AI algorithms, deep learning architectures, and advanced ML techniques will be crucial in driving innovative AI initiatives within the organization.

MAIN RESPONSIBILITIES:

• Accelerate GenAI & ML Adoption: Drive the adoption of Generative AI and Machine Learning technologies within company’s Commercial Organization by identifying high-value business challenges and developing scalable, impactful solutions that enhance productivity, decision-making, and innovation.
• Use Case Validation & Reusability: Collaborate with the AI & Automation Product Owner to validate backlog priorities, assess technical feasibility, and determine whether existing ML or GenAI assets can be scaled or adapted for new use cases.
• End-to-End Solution Delivery: Partner with cross-functional teams—including data scientists, ML/GenAI engineers, data/cloud experts, and RPA developers—to turn business problems into viable, sustainable solutions using ML algorithms or GenAI architectures (e.g., RAG, LLMs).
• Stakeholder Engagement: Work directly with business stakeholders to understand their needs, translate them into solution requirements, and provide actionable recommendations that align with business strategy. Ensure results from ML models or GenAI systems are clearly tied to tangible business outcomes.
• Model Development & Deployment: Design and build both predictive ML models and GenAI solutions—from data preprocessing and feature engineering to model development, fine-tuning, evaluation, and deployment—leveraging Azure, Databricks, and other enterprise platforms.
• GenAI-Specific Capabilities: Architect and implement Retrieval-Augmented Generation (RAG) systems, fine-tune LLMs, and apply advanced prompt engineering techniques to build chat-based tools, search augmentation solutions, and virtual assistants that deliver real business value.
• Performance Monitoring & Validation: Conduct rigorous model evaluation and performance monitoring for both ML and GenAI models to ensure reliability, fairness, and compliance. Continuously improve models based on feedback and production results.
• Communication of Insights: Translate complex ML/GenAI outcomes into business-friendly insights and visualizations. Present recommendations clearly to non-technical audiences and influence decision-making at multiple levels.
• Community Engagement & Coaching: Mentor junior team members and citizen developers, sharing GenAI/ML knowledge, reusable assets, and architectural patterns. Foster a culture of innovation and continuous learning within the CO AI & Automation community.
• Innovation & R&D: Stay updated with the latest research in ML and GenAI. Explore emerging technologies, tools, and techniques—such as vector databases, foundation model APIs, or self-supervised learning—that can enhance company’s AI maturity and solution portfolio.
• Manages vendor/technologies identification and selection process when external solutions are required.

JOB REQUIREMENTS (GenAI & ML Focus):

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Physics, Engineering, or a related quantitative field.
• Experience: Typically 4-6 years of hands-on experience developing and deploying ML and/or GenAI solutions in a business context, ideally within sales, operations, or enterprise functions.
• Programming & Development:
• Proficiency in Python for data manipulation, model development, and integration.
• Experience with PySpark and working in Databricks for scalable data pipelines and model training.
• Cloud & Platform Expertise:
• Strong hands-on experience with Azure services (e.g., Azure Machine Learning, Azure AI Search, Azure OpenAI, Azure Functions, Azure Kubernetes Service).
• Experience with containerization and deployment frameworks like Docker, AKS, or serverless options.
• ML/AI Model Development:
• Ability to design, train, and deploy supervised and unsupervised ML models.
• Skilled in model evaluation, hyperparameter tuning, and production monitoring.
• GenAI & NLP:
• Familiarity with Large Language Models (LLMs), prompt engineering, vector databases (e.g., FAISS, Pinecone), and Retrieval-Augmented Generation (RAG) architectures.
• Experience in building or integrating GenAI chat interfaces or knowledge assistants.
• Applied Statistics & Algorithms:
• Solid grasp of statistical modeling, feature engineering, and machine learning algorithms (e.g., regression, classification, clustering, time series).
• Business Acumen:
• Proven ability to understand business needs and align AI/ML solutions with strategic goals.
• Experience translating technical results into actionable business recommendations.
• Collaboration & Communication:
• Comfortable working in cross-functional teams.
• Strong communication skills to explain GenAI/ML results to both technical and non-technical stakeholders.
• Innovation Mindset:
• Eagerness to explore the latest in GenAI and ML, including foundation models, self-supervised learning, and applied research.
• Preferred:
• Experience with MLOps and model lifecycle management.
• Exposure to ethical AI practices, responsible AI frameworks, or compliance in enterprise settings.

KNOWLEDGE & SKILLS:

• Proper understanding of digital industry trends (RPA, AI, ML) and their benefits.
• Advanced communication skills (i.e. written, verbal, presentation) and collaboration skills to work effectively within a multidisciplinary team.
• Excellent problem-solving skills and ability to think creatively to tackle complex AI challenges.
• Proven experience working in globally distributed virtual teams
• Proficiency in English is a must
• Ability to effectively communicate data insights and negotiate options at senior management levels.
• Ability to work effectively with both technical and non-technical stakeholders to ensure successful AI/ML implementation
• Excellent program management, problem solving, and analytical skills, and the ability to execute a program within the scope of overall strategies.
• Team player