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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist Mid-Level - **Company:** Valtech - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Business Analytics Applications, Data Analysis, Microsoft Azure, BigQuery, Cloud Computing, Encodings, Computer Programming, Data Validation, Decision Support Systems, Github, Revision Control Systems, Python (Programming Language), Machine Learning, NumPy, Pattern Recognition, Power BI, Cloud Services, Standard Sql, Azure Machine Learning, SciPy, Tableau (Software), Unstructured Data, Management of Software Versions, Jupyter Notebook, Google Cloud, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Model Validation, Git, Pandas, Matplotlib, AI Platforms, Pyspark, Scikit Learn, Statistics Packages, Xgboost, Plotly, Machine Learning Operations, Feature Extraction, Text Analysis, Looker Analytics, Data Pipelines, Unsupervised Learning, Databricks - **Published:** August 30, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pg2lq0wj7e ## About 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), * 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., 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 recommendations. Understands that AI-generated code, modeling suggestions, analytical summaries, or methodological recommendations must be reviewed against source data, assumptions, statistical rigor, business context, governance expectations, and reproducibility standards before use. Demonstrates curiosity and practical enthusiasm for applying AI to improve analytical leverage, decision support, and delivery quality without weakening scientific discipline or human judgment. At this level, AI fluency means reliable use within workstreams. Expected to apply approved AI-assisted workflows to improve delivery quality, repeatability, documentation, and stakeholder communication, and to help junior practitioners understand where AI can and cannot be used responsibly. ## Description * 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., 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. ## Related Videos - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Vectorize all the things! 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