Senior Data Scientist / Staff Data Scientist

As a Senior Data Scientist at StackAdapt, your primary responsibilities include innovating machine learning algorithms to enhance advertising performance, writing production code, and collaborating closely with Data Engineers to implement your cutting-edge algorithms. You will also prototype algorithms, test them with historical data, and refine them based on actionable insights, all while working in a fast-paced, collaborative team environment.

To apply for the Senior Data Scientist role at StackAdapt, you should have a Master's degree or PhD in Computer Science, Statistics, Operations Research, or a related field. A solid understanding of statistics, machine learning, and optimization is crucial, along with strong coding skills and knowledge of data structures and algorithms. Having dual degrees or experience in a similar field is a plus.

StackAdapt offers a friendly, supportive, and inclusive culture that values diversity and teamwork. As a Senior Data Scientist, you will work in a collaborative environment that encourages innovation and personal development. The company has received recognition for being a great workplace and promotes initiatives like social events and personal development conferences.

Yes, StackAdapt believes in investing in its employees' growth. As a Senior Data Scientist, you will have access to coverage for personal development initiatives, including attending conferences, courses, and other learning opportunities to enhance your skills and expertise in the data science field.

Absolutely! StackAdapt is a remote-first company, and the Senior Data Scientist position can be performed from anywhere in the UK. This flexibility allows you to maintain a healthy work-life balance while contributing to a vibrant and diverse team from the comfort of your home.

When optimizing machine learning models, I typically use a combination of techniques such as hyperparameter tuning, feature selection, and cross-validation to assess model performance. I also focus on implementing algorithms that can adapt to varying datasets and utilize metrics like precision and recall to guide improvements.

When faced with ambiguous requirements, I start by breaking down the task into smaller, manageable components. I seek clarification through discussions with stakeholders, gather feedback, and outline actionable steps that guide my work while remaining flexible to adapt as more information becomes available.

Certainly! One approach is to compare machine learning models to teaching a child. Just like you would provide examples and feedback to help the child learn, machine learning models learn from data. The better and more diverse the examples (data), the better the model can make predictions, just like a child who becomes more skilled with practice.

I have experience using various big data technologies such as Hadoop and Spark for processing large datasets. In my previous projects, I utilized these tools to efficiently manage, analyze, and derive insights from extensive data, ensuring that models scale effectively under heavy workloads.

To ensure the reliability of data pipelines, I implement thorough testing at each stage of the pipeline, establish monitoring metrics for data quality, and set up alerts to identify issues proactively. Additionally, I conduct regular audits of the pipeline to ensure it meets the needs of ongoing analytics accurately.

One challenging data problem I addressed involved integrating disparate data sources while maintaining data integrity. I developed an ETL process that standardized formats and cleaned the data, ultimately enabling accurate analysis and enhancing our decision-making capabilities.

I prefer using tools like Tableau and Matplotlib for data visualization as they allow for interactive and clear representation of complex data sets. Tableau is particularly adept at creating dashboards for stakeholders, while Matplotlib is great for customizing visualizations during exploratory data analysis.

I keep my skills current by regularly engaging in online courses, participating in webinars, and attending conferences. I also follow influential data science blogs and communities, such as Kaggle and Medium, to stay on top of emerging tools and trends.

Statistics play a critical role in my data science work, as I use them for exploratory data analysis, hypothesis testing, and building predictive models. An understanding of statistical concepts helps me validate findings and ensure the reliability of my conclusions.

I am motivated by the power of data to drive decisions and innovations. The ability to derive meaningful insights from data and the challenge of solving complex problems excite me. I love being part of a field that is continually evolving, offering endless opportunities for learning and growth.