Lead Data Scientist

Company Description:
Dynatrace exists to make software work perfectly. Our platform combines broad and deep observability and continuous runtime application security with advanced AIOps to provide answers and intelligent automation from data. This enables innovators to modernize and automate cloud operations, deliver software faster and more securely, and ensure flawless digital experiences.
We're an equal opportunity employer and embrace all applicants. Dynatrace wants YOU-your diverse background, talents, values, ideas, and expertise. These qualities are what make our global team stronger and more seasoned. We're fueled by the diversity of our talented employees
Your role at Dynatrace:
Our Business Insights team is looking for a Lead Data Scientist to drive predictive analytics and causal inference solutions using large-scale tabular data. In this hands-on technical leadership role, you'll build sophisticated machine learning models to predict system performance impacts while ensuring we can understand the "why" behind the predictions through causal reasoning.
You'll work with complex, multidimensional datasets from application performance monitoring to help customers optimize their applications through data-driven insights.
What You'll Work With:

• Large tabular datasets: Multi-million row datasets with hundreds of features from application performance monitoring.
• Complex data challenges: High-cardinality categorical variables, time series components, and some data quality issues requiring sophisticated feature engineering.
• Predictive + causal modeling: Building models that not only predict outcomes but can explain causal relationships between system performance and business metrics.
• Production constraints: Models deployed at scale, serving predictions for enterprise customers.

Your Tasks:
Technical Leadership & Architecture

• Design and architect scalable machine learning solutions for tabular data with built-in causal interpretability.
• Establish technical standards for feature engineering, model validation, and causal inference methodologies.
• Lead code reviews and drive technical discussions on complex modeling challenges.
• Make architectural decisions for production ML systems that balance predictive accuracy with causal understanding.
• Provide technical guidance on balancing predictive performance with interpretability.
• Mentor new data scientists joining the team as we scale.

Hands-on Data Science

• Build and deploy predictive models using gradient boosting frameworks (XGBoost, LightGBM, CatBoost) while incorporating causal inference principles
• Develop sophisticated feature engineering pipelines for time series, behavioral, and performance data
• Design and implement experiments and quasi-experimental approaches to validate causal relationships
• Collaborate with ML Engineers to deploy and monitor models in production environments
• Drive data preprocessing initiatives, transforming complex, messy data into model-ready datasets

Minimum Qualifications:

• Advanced degree in Statistics, Economics, Computer Science, Mathematics, or a quantitative field with a strong statistical foundation
• 7+ years of data science experience with demonstrated technical leadership or senior IC experience
• Expert-level Python skills for data processing and ML model development (Pandas, NumPy, Scikit-learn, and experience with large datasets)
• Advanced SQL proficiency including complex queries, window functions, and query optimization
• Proven expertise with gradient boosting frameworks (XGBoost, LightGBM, CatBoost) and deep understanding of hyperparameter tuning and model interpretability
• Strong experience with causal inference methodologies (A/B testing, quasi-experimental designs, instrumental variables) and ability to integrate causal thinking into predictive modeling
• Advanced feature engineering skills, including handling high-cardinality categorical variables, missing values, and time series features
• Experience with model deployment and monitoring in production environments
• Proven ability to communicate complex technical concepts to diverse audiences

Desirable experiance:

• Experience with big data technologies (Spark, Hadoop, Presto, Snowflake).
• Advanced knowledge of ensemble methods, stacking, and model blending techniques.
• Familiarity with MLOps and model monitoring in production.
• Time series forecasting and anomaly detection experience.
• Experience mentoring junior data scientists.
• Background in application performance monitoring or DevOps.
• Knowledge of cloud platforms (AWS, Azure, GCP).
• Experience with LLMs or NLP techniques.

Why you'll love being a Dynatracer:

• A one-product software company creating real value for the largest enterprises and millions of end customers globally, striving for a world where software works perfectly
• Working with the latest technologies and at the forefront of innovation in tech on scale, but also in other areas like marketing, design, or research
• Working models that offer you the flexibility you need, ranging from full remote options to hybrid ones combining home and in-office work

• A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries
• An environment that fosters innovation enables creative collaboration and allows you to grow
• A globally unique and tailor-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals
• A truly international mindset with Dynatracers from different countries and cultures all over the world, and English as the corporate language that connects us all
• A culture that is being shaped by our global team's diverse personalities, expertise, and backgrounds
• A relocation team that is eager to help you start your journey to a new country, always there to support and by your side. If you need to relocate for a position you're applying for, we offer you a relocation allowance and support with your visa, work permit, accommodation, language courses, and a dedicated buddy program