Technical Manager, Data Science

As a Technical Manager in Data Science at StackAdapt, your main responsibilities include innovating machine learning algorithms for advertising optimization, designing and managing scalable real-time data pipelines, and working closely with both Data Scientists and Data Engineers to tackle complex data challenges. You will also prototype and test algorithms using historical data, coordinate architectural improvements for big data pipelines, and drive projects from conception to execution.

To excel as a Technical Manager in Data Science at StackAdapt, candidates should possess a deep understanding of algorithm and software design, along with a solid background in implementing probabilistic or machine learning algorithms. A high GPA from a reputable Computer Science program or significant experience in a competitive tech company is preferred. Additionally, strong problem-solving skills and the ability to lead complex projects collaboratively are essential.

The Technical Manager position at StackAdapt is vital in fostering a collaborative and supportive team culture. You will lead a group of Data Scientists and Engineers, encouraging innovation and open communication while respecting diverse perspectives. As part of a dynamic and inclusive workplace, you will be instrumental in promoting ongoing learning and development opportunities within the team.

StackAdapt offers a remote-first working environment that allows flexibility across the UK. The company promotes a friendly and welcoming culture, where collaboration and teamwork are emphasized. You will have opportunities to engage in social events and personal development initiatives, all while working on exciting projects that leverage your data science skills.

As a Technical Manager in Data Science at StackAdapt, you'll enjoy numerous growth opportunities, including leading significant projects, participating in training programs, and attending conferences. The role also allows you to collaborate with other talented professionals in the industry, enhancing your skills and paving the way for further career advancement within a highly innovative organization.

When designing scalable data pipelines, I start by analyzing the data flow requirements and identifying key processes that need optimization. I focus on modular design for better maintainability and scalability, leveraging tools like Apache Kafka or Spark for real-time processing. Additionally, I implement performance monitoring to identify bottlenecks and ensure the pipeline can handle increasing volumes of data efficiently.

I have experience implementing a range of machine learning algorithms, from supervised learning techniques like regression and classification to unsupervised methods such as clustering. I prioritize understanding the problem domain to select and customize algorithms effectively, ensuring they solve specific business challenges, especially in marketing and advertising contexts.

In my previous role, I managed a project that involved developing a real-time recommendation engine for a digital marketing campaign. I coordinated cross-team collaboration to gather requirements, designed data pipelines for processing user interactions, and led the implementation of machine learning models that significantly improved engagement metrics.

To ensure low latency in algorithms, I focus on streamlining data processing workflows by using efficient data structures and caching frequently accessed data. I also conduct rigorous performance testing and profiling to identify slow components, iterating on the design to enhance speed without compromising accuracy.

I believe in fostering an open communication culture and regular check-ins to align on project goals. I use collaborative tools for documentation and task management, ensuring everyone is on the same page. Encouraging team members to share ideas and solutions helps us tackle challenges more effectively.

I regularly engage with industry publications, attend conferences, and participate in webinars to keep abreast of the latest trends and technologies in data science. Additionally, I am active in online forums and communities where professionals share insights and discuss new research.

I have substantial experience working with big data technologies such as Hadoop and Spark for processing large datasets. My responsibilities included setting up and optimizing distributed systems, ensuring they are configured for performance and reliability while meeting the system's data requirements.

A significant challenge I've faced was dealing with data quality issues in a project. To tackle this, I implemented data validation frameworks to identify and rectify inconsistencies early in the pipeline. Additionally, I established best practices for data governance to maintain high data integrity moving forward.

I prioritize tasks based on business impact and urgency. Utilizing agile methodologies, I break projects into manageable sprints and regularly reassess priorities during team meetings to ensure we stay aligned with our goals and can adjust as new challenges arise.

The most important qualities of a Technical Manager include strong technical expertise, effective communication skills, and the ability to inspire and lead a team. Balancing technical prowess with interpersonal skills enables a manager to facilitate collaboration and drive innovation within the team.