- Страна
- Польша
- Зарплата
- 320 000 ₽ – 350 000 ₽
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Staff GTM Data Scientist
Отличная позиция в известной продуктовой компании с сильной культурой. Высокий уровень ответственности, работа с современным стеком и конкурентная зарплата для рынка Польши.
Сложность вакансии
Роль уровня Staff требует не только глубоких технических знаний в области причинно-следственного вывода (Causal Inference), но и лидерских качеств для управления изменениями и менторства. Высокая планка ожиданий по части влияния на бизнес-решения и работу с топ-менеджментом.
Анализ зарплаты
Предложенная зарплата в 320,000–350,000 PLN в год является очень конкурентоспособной для рынка Польши и соответствует уровню Staff/Lead Data Scientist. Это выше среднего медианного значения для Senior ролей, что подчеркивает высокую значимость позиции.
Сопроводительное письмо
I am writing to express my strong interest in the Staff GTM Data Scientist position at PandaDoc. With over 6 years of experience in applied data science and a deep specialization in causal inference and experimentation frameworks, I have consistently delivered actionable insights that drive product growth and operational efficiency. My background in designing complex A/B tests and applying quasi-experimental methods aligns perfectly with PandaDoc's mission to scale its experimentation roadmap.
In my previous roles, I have not only built scalable analytical assets but also mentored junior talent and influenced C-suite decisions through compelling data storytelling. I am particularly drawn to PandaDoc’s transparent culture and the challenge of mapping low-level product metrics to high-level business outcomes. I am confident that my technical expertise in Python, SQL, and causal modeling, combined with my strategic mindset, will make a significant impact on your GTM strategy.
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Откликнитесь в pandadoc уже сейчас
Присоединяйтесь к PandaDoc, чтобы возглавить стратегию экспериментов и влиять на развитие глобального SaaS-продукта!
Описание вакансии
Company Overview
PandaDoc empowers more than 60,000 growing organizations to thrive by taking the work out of document workflow. PandaDoc provides an all-in-one document workflow automation platform that helps fast scaling teams accelerate the ability to create, manage, and sign digital documents including proposals, quotes, contracts, and more. For more information, please visit https://www.pandadoc.com.
What You'll Do
Experimentation & Causal Strategy
- Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).
- Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.
- Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.
- Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.
- Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.
Technical Leadership & Influence
- Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers.
- Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities.
- Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.
About You
Qualifications
- Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
- Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.
Required Technical Expertise
- Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
- Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
- Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
- Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
- Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
- Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.
Key Attributes
- Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
- Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
- Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
- Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.
Company Culture
We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. Our attainment of awards such as Best Workplace, Best StartUp Employer and a Stevie Award for Best Employers demonstrate our commitment to our culture.
Check out LinkedIn to learn more.
Benefits
- Competitive salary (If you are located in Poland the salary range is 320-350 PLN annually/gross)
- Remote-first approach with the option for hybrid work from our offices in Kyiv, Warsaw, and Lisbon.
- We value long-term collaboration, whether through typical employment contract, employment of record or B2B arrangements. Be aware that contract type and benefits vary by location - feel free to clarify with our recruiters).
- Work schedule aligned with EU time zones.
- Honest, open culture that values constructive feedback.
- Professional and personal development within a collaborative, supportive team.
- Stable yet growing SaaS product offering an agile environment, ownership, start-up energy, and strong technical challenges.
PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.
EXTERNAL RECRUITERSApproval RequirementThe use of external recruiters/staffing agencies requires prior approval from our HR Team. The HR Team at PandaDoc requests that external recruiters/staffing agencies not to contact PandaDoc employees directly in an attempt to present candidates. Complying with this request will be a factor in determining future professional relationships with PandaDoc.
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Навыки
- Python
- R
- SQL
- Causal Inference
- A/B Testing
- Statistics
- Machine Learning
- Snowflake
- PostgreSQL
- dbt
- Airflow
- Databricks
- Scikit-learn
- NumPy
- Pandas
Возможные вопросы на собеседовании
Проверка глубины знаний в области причинно-следственного вывода, когда классический A/B тест невозможен.
Расскажите о случае, когда вам приходилось использовать квазиэкспериментальные методы (например, Difference-in-Differences или синтетический контроль). Каковы были основные допущения и как вы их проверяли?
Оценка навыков работы с шумом в данных и повышения чувствительности тестов.
Какие методы снижения дисперсии, такие как CUPED, вы применяли на практике и какой эффект это дало для скорости принятия решений?
Проверка способности выстраивать иерархию метрик.
Как бы вы спроектировали систему KPI, которая связывает краткосрочные продуктовые метрики (например, вовлеченность) с долгосрочными бизнес-целями, такими как LTV или удержание?
Оценка лидерских качеств и навыков коммуникации.
Опишите ситуацию, когда результаты вашего анализа противоречили мнению руководства. Как вы выстраивали коммуникацию, чтобы убедить стейкхолдеров в своей правоте?
Проверка архитектурного мышления в аналитике.
Как вы подходите к созданию масштабируемых инструментов для самообслуживания (self-serve) в области экспериментов для других команд?
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- Страна
- Польша
- Зарплата
- 320 000 ₽ – 350 000 ₽