Data Scientist Mid-Level

Why Valtech ? We’retheexperience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience. The opportunity At Valtech , you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries. We are proud of: The work we do and the innovation we drive Our values of share, care and dare A workplace culture that fosters creativity, diversity and autonomy Our borderless, global framework, which enables seamless collaboration The role As a Data Scientist , you are passionate about experience innovation and eager to push the boundaries of what’s possible. You bring 3+ YEARS of experience, a growth mindset and a drive to make a lasting impact. You will thrive in this role if you are: A curious problem solver who challenges the status quo A collaborator who values teamwork and knowledge-sharing Excited by the intersection of technology, creativity and data Experienced in Agile methodologies and consulting (a plus) Role responsibilities Lead the development of analytical, statistical, machine learning, and applied AI solutions for business and client use cases. Translate business questions into structured analytical approaches, modeling strategies, hypotheses, features, evaluation methods, and measurable outputs. Design and execute analyses and models across use cases such as segmentation, forecasting, propensity modeling, anomaly detection, experimentation analysis, recommendation-oriented analysis, and decision support. Work independently with structured, semi-structured, and selected unstructured datasets to derive insights and develop business-relevant solutions. Build, refine, and maintain notebook-based workflows and reproducible analytical assets in Databricks and other cloud-based environments. Apply machine learning and AI methods to support classification, scoring, summarization, pattern detection, feature generation, and business process improvement use cases. Support the evaluation and practical application of LLM-enabled or AI-assisted workflows where they strengthen business analysis, insight generation, or decision support. Participate in model training, tuning, validation, performance review, and comparative evaluation across different analytical and AI approaches. Document assumptions, methodology, feature logic, model decisions, evaluation criteria, limitations, and findings clearly and consistently. Partner with Data Analysts, AI Scientists, AI Engineers, Analytics Engineers, Data Engineers, and Architects to ensure solutions align with business needs, data realities, and technical constraints. Improve delivery quality by identifying opportunities for better reproducibility, stronger evaluation practices, clearer documentation, and more scalable analytical workflows. Follow established governance, privacy, and responsible data and AI use standards in day-to-day work. Must have qualifications To be considered for this role, you must meet the following essential qualifications: Strong working knowledge of statistics, probability, machine learning, and analytical problem solving. Ability to independently manage recurring data science workstreams and deliver reliable outputs with minimal oversight. Strong understanding of supervised and unsupervised learning approaches, feature engineering, model evaluation, error analysis, and analytical problem framing. Ability to work effectively with structured, semi-structured, and selected unstructured datasets. Working knowledge of experimentation design, model validation, and the interpretation of analytical and predictive outputs in business contexts. Growing familiarity with applied AI methods, including LLM-enabled workflows, text-oriented analysis, and AI-assisted feature extraction or classification. Strong familiarity with notebook-based development and collaborative data science workflows, including Databricks. Strong curiosity about patterns, behaviors, drivers, and how advanced analytical methods support decision-making and business value. Strong attention to detail and disciplined approach to validating data, logic, methodology, and outputs. Strong written and verbal communication skills in English, including the ability to explain analytical methods and findings clearly to non-technical stakeholders. Ability to balance technical rigor with practical business and delivery realities. Ability to collaborate effectively across distributed teams in the Americas and work across functions, time zones, and client contexts. Tools / Platforms Programming / Data Science Python Jupyter Notebooks Pandas NumPy scikit-learn SciPy Statsmodels XGBoost LightGBM Data Science Workbench / Lakehouse