Senior Data Scientist

Sojern
London
1 month ago
Create job alert

Overview

Senior Data Scientist role at Sojern. We are seeking a data-driven scientist to join Sojern's Data Science team within the Science and Optimization group to improve customer KPIs through experimentation, measurement, and analytics, and to drive impact for Sojern’s marketing platform using state-of-the-art causal inference and advanced statistical methods.

Team

The Data Scientist team within Sojern’s Science and Optimization group plays a crucial role in improving customer KPIs through experimentation, measurement, and analytics, and driving significant impact for Sojern’s marketing platform. The team uses state-of-the-art causal inference techniques and advanced scientific methods to continuously enhance the performance, delivery and efficiency of Sojern’s AI-powered marketing products.

Position summary

We are seeking a Senior Data Scientist who is highly detail-oriented and data-driven. This expert will collaborate with Product Managers, Software Engineers, Data Analysts, and Applied Scientists to shape the product roadmap and drive measurable results for thousands of Sojern clients. The ideal candidate will be skilled in statistical concepts, causal inference, SQL and Python, and capable of working independently.

Responsibilities

  • Collaborate across functional teams, including Product Managers, Software Engineers, Analysts, and Scientists to enhance the performance, delivery, and efficiency of Sojern’s AI-driven marketing products.
  • Lead scientific domain in experimentation projects from scoping, test design, implementation, measurement, and insights delivery.
  • Apply statistical analysis, hypothesis testing, quasi-experimental techniques to rigorously evaluate experiments and draw actionable and data-driven conclusions.
  • Use advanced scientific techniques such as Bayesian inferences, predictive modeling, and optimization to identify trends and develop algorithms that optimize bidding strategies across marketing channels.
  • Collaborate with engineers to deploy models in production and with analysts to embed insights into strategic decision-making.
  • Communicate findings and recommendations to both technical and non-technical stakeholders, enabling faster and more informed decision-making.
  • Identify opportunities to improve methodologies and processes as well as develop scalable solutions that drive impact, e.g. productionize modeling codes, build recyclable reporting templates.
  • Contribute to data-driven product roadmap and scientific project prioritization.
  • Train and mentor analyst members on scientific methodologies as needed to support team growth.

Qualifications

  • Strong problem-solving and analytical skills to understand business problems, develop hypotheses, work with large datasets, and apply appropriate methodologies to drive data-informed decisions.
  • Intermediate/advanced proficiency in analytical tools and techniques:
    • SQL expertise for data collect, process, and analysis to identify trends and patterns.
    • Python proficiency (e.g. pandas, statsmodels, sklearn, numpy, scipy, and other related libraries/packages) for data visualization, data analysis, and statistical modeling.
    • Spreadsheet usage (e.g. Microsoft Excel, Google Sheets), including pivot functions and complex formulas.
  • 2+ years of experience as a data scientist, ideally within the advertising technology ecosystem.
  • Strong understanding of statistical concepts, measurement methods, and causal inference techniques (e.g. randomized control trials, difference-in-differences, synthetic control modeling, time-series forecasting, hierarchical Bayesian, linear/nonlinear regression).
  • Effective verbal and written communication skills to collaborate cross-functionally and present insights to technical and non-technical audiences, influencing product roadmaps.
  • Curiosity and initiative with a mindset of asking “why”, and proposing ideas that drive incremental impact.
  • Ability to collaborate across time-zones and adjust priorities as needed.
  • Attention to detail to ensure high quality of delivery in analytical tasks, documentation, and business requirement gathering.
  • MS or PhD in a quantitative discipline (e.g. Economics, Statistics, Data Science, Applied Mathematics, Computer Science, Operations Research).
  • Preferred: knowledge of digital marketing (e.g. paid media, targeting, attribution, impression, CPM, click through rate) and experience with Tableau or other visualization software.

What we have for you

We take a whole-person approach to create a Sojernista Experience that allows our people to thrive, not just as employees, but as humans. As an employee of Sojern, you would benefit from this in the following ways:

  • Rewards & Recognition: Competitive compensation packages, stock options, Bonusly program to reward and recognize team wins and performance, plus employees can take up to 40 hours of paid time per year to volunteer and give back to the community
  • Flexibility: Flexi-Friday benefit, hybrid or remote work options for most roles, time-zone friendly work hours with async collaboration
  • Connection: Team offsites planned annually, six employee resource groups, regular virtual and in office team building events, monthly company All Hands & leadership Q&As
  • Wellbeing: PTO allowance to recharge, comprehensive healthcare options, paid parental leave (16 weeks for birthing parents; 12 weeks for non-birthing parents), retirement contributions and investment options (for applicable locations), travel benefits (hotel stay benefit & IATA membership), plus mental health, wellness & financial health resources
  • Growth: Learning & development stipend, mentorship program, career development programs, leadership training
  • Productivity: Home office tech set up (laptop, monitor, keyboard, mouse), monthly internet and phone allowance, modern tools to communicate and collaborate (Slack, Google Suite)

Our Sojernista First workplace philosophy

We design a flexible approach that recognizes diverse needs while focusing on belonging, collaboration, and wellbeing for all Sojernistas, wherever you work.

About Sojern

Sojern builds smart digital solutions that help travel marketers reach travelers efficiently, increasing growth, loyalty, and profitability. Our customers include hotels, attractions, and tourism boards, who use machine learning, data science, and real-time traveler data in Sojern’s products to engage travelers across social, mobile, and the web. We are globally distributed, headquartered in San Francisco, with employees in 14 countries.

Company values

We win as a team, embrace inclusion, be genuine, deliver wow, and center around the customer. We foster diversity and inclusion across the company and support employee resource groups: SoEmpowered, SoProud, SoWell, SoConnected, Parents & Caregivers, Sojern Gives Back. We also have employee channels for shared interests. Sojern is an equal opportunity employer and commits to reasonable accommodations on request.

Job details

  • Seniority level: Mid-Senior level
  • Employment type: Full-time
  • Job function: Engineering and IT
  • Industries: Hospitality


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist (GenAI)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.