About the job
As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics.
Responsibilities
Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies.
Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions.
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales.
Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization.
Translate complex scientific challenges into clear and impactful solutions for business stakeholders.
Mentor and guide junior scientists, fostering a collaborative and high-performing team culture.
Foster collaborations between scientists to move faster, with broader impact.
Regularly engage with the broader scientific community with presentations, publications, and patents.
Qualifications
Minimum
3+ years of building machine learning models for business application experience
PhD, or Master's degree and 6+ years of applied research experience
Knowledge of programming languages such as C/C++, Python, Java or Perl
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
Preferred
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.