At **Mindera**, we believe that software is built by people, for people with high performance systems that impact users worldwide. We are looking for a **Senior** **Data Scientist** to join an agile, collaborative team where your voice matters as much as your code. The **Data Scientist** is a high-level individual contributor who leads complex data science initiatives that impact the Core Tech roadmap. This role focuses on translating complex marketing and product challenges into data-driven recommendations. Working with significant independence, the **Senior Data Scientist** develops predictive models and performance reporting frameworks that enable stakeholders across the organization to make informed investment decisions. We value **empathy, self-organization, and the courage to take risks.** If you love solving complex problems and believe that teaching others is the best way to learn, you’ll feel right at home here. We’re looking for someone who loves to build, learn, and guide others. You’ll be working closely with our leadership (CEO/COO) and our clients, so clear communication and a collaborative spirit are key. **💪 How You'll Contribute** - **Business Insight & Problem Definition:** Partner closely with marketing and product teams to diagnose business challenges and define appropriate data metrics, analyses, and interpretations to drive strategic decisions. - **Statistical Modeling:** Define requirements for and lead the creation, monitoring, and iterative improvement of key statistical models used for marketing spend optimization and product growth. - **Narrative & Visualization:** Design clear, unambiguous narratives and data visualizations to communicate model outputs and analysis results to broad, non-technical audiences. - **Rapid Prototyping:** Independently prototype and test new insights, moving from hypothesis to rigorous validation with minimal mentorship. - **Technical Consultation:** Reason effectively about the strengths and weaknesses of different modeling approaches, selecting and iterating on combinations that suit specific marketing contexts.

Role Description We are looking for an experienced Senior Data Scientist to join a high-impact Ads Data Science team focused on scaling and optimizing a rapidly growing advertising marketplace. Advertising is a core revenue driver, and this role sits at the heart of building a high-performance, data-driven marketplace ecosystem — where advertisers, users, and platform dynamics interact in real time. You will work on problems such as auction dynamics, pricing, targeting, relevance, and marketplace efficiency, leveraging large-scale data and advanced modeling techniques to drive measurable business impact. Commitments Required: 8 hours per day with an overlap of 4 hours with PST. Employment type: Contractor or FTE Duration of contract: long-term, but with 3-month increments based on performance Responsibilities - Design and apply data science and machine learning solutions to improve ads marketplace performance - Analyze large-scale datasets to uncover patterns in marketplace behavior (bidding, pricing, engagement, conversion) - Develop models for prediction, anomaly detection, and optimization - Define and track core marketplace metrics (supply/demand balance, auction efficiency, advertiser value) - Build data assets (tables, dashboards, self-serve tools) to empower product and engineering teams - Partner with Product and Engineering to translate marketplace challenges into DS solutions - Design and analyze A/B experiments and causal inference frameworks to validate impact - Communicate insights and recommendations to cross-functional stakeholders Qualifications - 5+ years of experience in Data Science, Machine Learning, or Applied Statistics - Strong experience working on marketplace platforms (e.g., ads platforms, ride-sharing, e-commerce, or similar two-sided ecosystems) - Deep understanding of marketplace dynamics (auction systems, pricing, supply/demand, ranking, or allocation problems) - Strong foundation in statistical modeling, machine learning, and experimental design - Experience with large-scale data processing (e.g., Spark, Hadoop, Hive, or BigQuery) - Advanced proficiency in SQL and working with large relational datasets - Strong programming skills in Python (preferred) or R - Experience building and interpreting A/B tests and causal inference models - Ability to work with cross-functional teams and influence product decisions through data Nice to Have - Experience in online advertising / ad tech ecosystems - Experience with auction optimization, bidding strategies, or ranking systems - Familiarity with real-time systems or large-scale recommendation systems - Experience building self-serve analytics tools or data platforms

• Design and apply **data science and machine learning solutions** to improve ads marketplace performance • Analyze large-scale datasets to uncover **patterns in marketplace behavior** (bidding, pricing, engagement, conversion) • Develop models for **prediction, anomaly detection, and optimization** • Define and track **core marketplace metrics** (supply/demand balance, auction efficiency, advertiser value) • Build **data assets** (tables, dashboards, self-serve tools) to empower product and engineering teams • Partner with Product and Engineering to **translate marketplace challenges into DS solutions** • Design and analyze **A/B experiments and causal inference frameworks** to validate impact • Communicate insights and recommendations to **cross-functional stakeholders**

• Lead and build a new analytics team that drives product growth through data-driven insights and AI-powered analysis. • Define and execute the analytics roadmap, guiding proactive analyses to uncover growth opportunities. • Build and mentor a high-performing team of analysts, establishing excellence in SQL, visualization, and statistical methods. • Champion AI integration to optimize analytical workflows and unlock deeper product insights. • Partner closely with Product Managers as their primary data ally, translating complex findings into clear, actionable recommendations. • Collaborate with data engineers and implementation specialists to prioritize tracking, define schemas, and guide data modeling. • Enable broader analytics teams through training on behavioral data usage, querying best practices, and dataset documentation.