Lead Data Scientist / Software Engineer
About the Role
This is a unique opportunity to combine cutting-edge data science with advanced software engineering to create innovative, data-driven software solutions. In this role, you will take ownership of designing and building high-quality systems that leverage statistical models, machine learning techniques, and data analytics to tackle complex business challenges.
You'll engage with Python-based data science workflows as well as C#/.NET-based enterprise solutions on Azure. This position is perfect for someone who thrives on hands-on engineering while also deriving impactful insights from data to shape product decisions and strategy.
Key Responsibilities
Data Science & Advanced Analytics
- Design and execute experiments and A/B tests to test product and business hypotheses.
- Develop, validate, and fine-tune machine learning and predictive models.
- Conduct exploratory and root cause analyses to discover trends, anomalies, and key drivers.
- Lead the feature engineering process to enhance model performance and interpretability.
- Apply statistical and mathematical techniques to practical, real-world problems.
- Create clear and engaging visualizations and analytical reports for stakeholders.
- Ensure data quality, integrity, and consistency across various sources.
Software Engineering & System Implementation
- Design and implement robust services and applications that integrate data science outputs into production systems.
- Build and maintain C#/.NET (ASP.NET Core) APIs and backend components on Microsoft Azure.
- Develop data pipelines and processing logic for analytical and operational use cases.
- Collaborate with frontend engineers to bring data-driven experiences to life.
- Optimize the performance, reliability, and scalability of applications and data pipelines.
Data Platforms & Integration
- Prepare, preprocess, and optimize data for analysis and modeling using Python.
- Design and query SQL Server / Azure SQL databases for analytical and transactional workloads.
- Ensure seamless interaction between analytical models and software systems.
- Support model deployment, monitoring, and validation in production settings.
Quality, Testing & Responsible AI
- Contribute to testing strategies for data pipelines, models, and software components.
- Engage in peer reviews of code, models, and analytical methodologies.
- Evaluate AI and data tools with a thoughtful and responsible mindset.
- Implement validation and governance practices for AI-assisted code and model outputs.
Collaboration & Mentorship
- Communicate analytical findings and technical solutions effectively to both technical and non-technical audiences.
- Collaborate closely with product teams, engineering, and business partners.
- Mentor junior data scientists and engineers through knowledge sharing and constructive feedback.
- Participate in Agile planning, estimation, and delivery processes.
Core Skills & Qualifications
Programming & Engineering
- Strong expertise in C#/.NET for enterprise software development.
- Strong expertise in Python for data analysis, modeling, and pipeline creation.
- Familiarity with R, Java, or C++ is a bonus.
- Solid understanding of software engineering principles and system design.
Data & Analytics
- Advanced data analysis and problem-solving abilities.
- Hands-on experience with machine learning algorithms and predictive modeling.
- Proven experience applying analytics to drive product or business success.
- Expertise in feature engineering, model tuning, and validation processes.
- Proficiency with data visualization tools (e.g., Tableau, Power BI, Matplotlib).
Databases & Platforms
- Strong SQL skills and experience with relational data modeling.
- Familiarity with cloud platforms, preferably Microsoft Azure.
- Experience with NoSQL or large-scale data systems is an advantage.
Development Practices
- Experience working effectively in Agile environments.
- Strong testing, documentation, and teamwork practices.
- Ability to balance analytical depth with high-quality engineering.
Core Competencies
Our Interview Practices
To foster a fair and genuine hiring experience, all candidates are encouraged to participate in interviews without the use of AI tools or external prompts. This process aims to evaluate your unique skills, experiences, and communication style. We value authenticity and wish to connect with you, not a digital assistant. To maintain this integrity, we request that virtual backgrounds be removed and that in-person interviews be integrated into our hiring process. Please be aware that the use of AI-generated responses or third-party assistance during interviews may lead to disqualification from the recruitment process.
Applicants may need to visit a Wolters Kluwer office as part of th