Data Scientist Mid-Level

Why Valtech? We’re the experience 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 a nd 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 Platforms

Databricks

Databricks notebooks

Databricks Machine Learning

Apache Spark

PySpark

MLflow

Data & Querying

SQL

BigQuery

Snowflake

Other cloud data platforms as needed

Cloud & AI Platforms

Google Cloud Platform (GCP)

Vertex AI

Microsoft Azure

Azure AI services

Azure Machine Learning

Other cloud-based machine learning and analytics platforms as needed

Applied AI / LLM Support

OpenAI-compatible APIs or enterprise LLM platforms as relevant to the client environment

Prompt evaluation and structured testing workflows

Embedding, text analysis, and unstructured data processing patterns

Model and workflow evaluation tooling as relevant to the client environment

Visualization / Analysis Support

Matplotlib

Seaborn

Plotly

Looker

Power BI

Tableau

Workflow / Collaboration / Versioning

Git

GitHub

Azure DevOps

Other collaboration and code management tools as relevant to the client environment

Certifications

Preferred, not required

Databricks associate-level training or certification

Google Cloud data, ML, or AI training

Microsoft Azure data, ML, or AI training

Python, machine learning, experimentation, or applied AI coursework

Statistics, forecasting, or analytical modeling training

Collaboration / Stakeholder Expectations

Serves as a dependable data science partner to internal teams and client stakeholders across modeling, analysis, and applied AI needs.

Collaborates closely with Data Analysts to ensure analytical and predictive outputs connect clearly to reporting, decision-making, and business context.

Works with AI Scientists and AI Engineers where use cases involve LLMs, unstructured data, agentic patterns, or more advanced AI solution design.

Partners with Analytics Engineers, Data Engineers, and Architects to ensure workflows are supported by scalable data pipelines, governed structures, and reliable environments.

Participates confidently in client-facing discussions by explaining methodology, model logic, findings, limitations, and practical implications in clear business language.

Helps improve team consistency by strengthening notebooks, documentation, evaluation methods, and reusable analytical practices.

AI Fluency / AI-Assisted Data Science Expectations

Expected to be an active adopter of approved AI-enabled analytical, coding, experimentation, documentation, and productivity workflows that improve the quality and speed of data science work. Uses AI-assisted workflows to support exploratory analysis, feature thinking, code and notebook development, model documentation, experiment design, analytical summarization, and stakeholder communication while maintaining human accountability for method selection, statistical reasoning, validation, interpretation, and final recomme