Data Scientist - Applied AI (Go-To-Market)
Confirmed live in the last 24 hours
Trustpilot
Job Description
At Trustpilot, we're on an incredible journey. We're a profitable, high-growth FTSE-250 company with a big vision: to become the universal symbol of trust. We run the world's largest independent consumer review platform, and while we've come a long way, there's still so much exciting work to do. Come join us at the heart of trust!
*This is a Fixed-Term Contract (FTC) for a duration of 12 months.
From reviews to user behaviors to internal systems, at Trustpilot, we truly have big data. Every month, more than half a million new reviews are posted on Trustpilot for thousands of businesses. We have now reached 267 million reviews - and that is growing! - from consumers around the world. Our Data Scientists in the GTM Applied AI team leverage this data to build AI/ML models that improve our go-to-market (GTM) efficiency and enhance our pricing and revenue management strategy.
We are seeking a skilled Data Scientist to join our GTM Applied AI team to develop, deploy, and maintain our GTM and Pricing models. You will collaborate closely with our pricing and monetization squad and team of software developers, data analysts and ML engineers to develop, deploy, and maintain innovative AI/ML models at scale. You will be part of a unique journey where data, AI, and ML are at the heart of our product and company strategy. This role offers the opportunity to collaborate broadly across the business, including with our Commercial, Digital Sales, and Applied AI teams, in both B2B and B2C contexts.
To succeed in this role, you should have proven experience in developing and deploying AI/ML models to enhance commercial practices. You have a solid technical background, with hands-on ability in all stages of data preparation, exploration, and modeling. You must have an adaptable commercial mindset and knowledge of the interface between data science and engineering. The ability to develop AI/ML solutions, engage stakeholders, and clearly articulate the impact of your work is necessary for this role. Experience putting solutions into production within an interdisciplinary team is a big plus, and it will be ideal if you also have experience using AI and ML to solve pricing and revenue management problems.
What you’ll be doing:
- You will be involved in delivering some of our most exciting Data Science projects aimed at improving our GTM efficiency and pricing practices: from prediction to ranking, segmentation to NLP, and recommendation systems to content generation.
- You will deliver the Data Science component of key strategic initiatives, including owning, maintaining, and deploying production-ready ML/AI models, and analysing data to establish the scope and impact of your work.
- You will identify new pricing and monetization opportunities based on data, interpreting model outcomes, and share insights to drive the direction of our business goals.
- You will engage with both technical and non-technical stakeholders & will translate business requirements into Data Science deliverables.
- The opportunity to work with leading data engineering technologies, including Google Vertex AI tools, Google BigQuery, BigQuery Pipelines, and Airflow, with leading Data Science tools and emerging technologies for model building and deployment.
- Opportunities to develop your career in a friendly, diverse, innovative, international team and workplace.
Who you are:
- Experience with analytical and quantitative problem solving using advanced statistical techniques and machine learning methods, e.g. Attribution, Segmentation, Churn, Upgrade, Upsell, Pricing Optimisation, and LTV.
- Experience in building and deploying production-ready ML models, and solid data engineering skills (e.g. experience with cloud technologies - we use GCP, but experience with AWS is also relevant).
- Ability in Python and SQL for data manipulation, modelling, and scripting.
- You have experience working with large datasets. Knowledge of the data generated by websites or in the e-commerce sector, behavioral analytics, and experience working with data from commercial systems (e.g. Salesforce) is advantageous.
- Knowledge of data pipelining and prior experience with cloud-based ML model deployments are beneficial.
- Great communication skills - both with technical colleagues and with business stakeholders.
- Proven technical experience in a Data Science role, particularly in the technology sector or in
