Senior Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in Saudi Arabia. This is a senior, hands-on data science role focused on improving the accuracy and intelligence of a high-volume, real-time identification platform. You’ll work with complex, noisy, and often unlabeled data to develop machine learning approaches that distinguish browsers and devices with greater precision. The role combines advanced machine learning, statistical analysis, experimentation, and backend engineering in a highly technical environment. You’ll own projects from initial research and experimentation through production deployment and integration with real-time services. You’ll also help shape engineering practices, data-driven decision-making, and effective approaches to machine learning across the wider team. This is a fully remote opportunity suited to someone who enjoys solving challenging problems where research, software engineering, and applied ML meet. Accountabilities • Develop data-driven algorithms using raw, noisy, and unlabeled data to improve browser and device identification capabilities. • Design and implement supervised, semi-supervised, and unsupervised machine learning approaches, including methods for high-cardinality categorical data. • Own data science initiatives end to end, from problem definition and experimentation through production deployment and integration with real-time services. • Design experiments and technical solutions for real-time inference, model-to-service integration, training automation, and other machine learning engineering challenges. • Conduct exploratory data analysis to investigate business and technical questions, identify anomalies, and evaluate model and dataset performance. • Develop practical approaches for collecting and evaluating data when labeled datasets are limited or unavailable. • Contribute to an engineering-focused, data-driven culture by sharing tools, methodologies, and effective data science practices with colleagues. • Collaborate across data science and engineering functions to turn machine learning concepts into reliable, production-ready services. • Participate in a shared on-call rotation, with schedules communicated in advance and coverage designed to minimize disruption outside normal working hours. Requirements • 5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines. • Advanced knowledge of machine learning fundamentals and statistical methodologies. • Strong practical experience with supervised learning, including gradient boosting and approaches for high-cardinality categorical data. • Hands-on experience with semi-supervised and unsupervised learning techniques. • Strong exploratory data analysis and creative problem-solving skills, particularly when working with incomplete, noisy, or unlabeled datasets. • Proven experience developing real-time machine learning services, including model inference and integration between models and production applications. • Ability to transform machine learning models into minimum viable real-time web services and production-ready solutions. • Excellent coding and software engineering skills, including strong SQL capabilities and familiarity with Git, CI/CD pipelines, IDEs, and shell scripting. • Strong communication skills and fluent English, with the ability to collaborate effectively in a distributed international environment. • A research mindset and academic background are advantageous. • Experience with Go and backend development is a plus. • Familiarity with analytical data platforms such as ClickHouse, Snowflake, or BigQuery is beneficial. • Experience with data transformation frameworks such as dbt, visualization tools such as Superset, Tableau, or Looker, or vector databases such as Pinecone, FAISS, or Qdrant is a plus. • Experience building embedding-based search systems is advantageous. • Familiarity with technologies such as Go, SQL, advanced ML frameworks, ClickHouse, dbt, and AWS is beneficial. Benefits • Competitive compensation, with the source role indicating a US cash compensation range of $152,000–$205,000 USD; compensation ranges are location-specific and may differ for candidates based in India. • Fully remote working environment. • Opportunity to work on challenging machine learning problems involving real-time systems, large-scale data, and advanced device intelligence. • Exposure to modern machine learning, data engineering, backend development, and cloud infrastructure technologies. • Opportunity to contribute to technical strategy and influence engineering and data science practices. • Collaboration with a globally distributed team. • Inclusive environment that values diverse experiences, perspectives, and backgrounds. • Candidates must be authorized to work from their home location; visa sponsorship is not provided. • Participation in a shared on-call rotation with advance scheduling and an effort to balance coverage fairly while minimizing off-hours disruption. How Jobgether Works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Show more

5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines. Advanced knowledge of machine learning fundamentals and statistical methodologies. Strong practical experience with supervised learning, including gradient boosting and approaches for high-cardinality categorical data. Hands-on experience with semi-supervised and unsupervised learning techniques. Strong exploratory data analysis and creative problem-solving skills, particularly when working with incomplete, noisy, or unlabeled datasets. Proven experience developing real-time machine learning services, including model inference and integration between models and production applications. Ability to transform machine learning models into minimum viable real-time web services and production-ready solutions. Excellent coding and software engineering skills, including strong SQL capabilities and familiarity with Git, CI/CD pipelines, IDEs, and shell scripting. Strong communication skills and fluent English, with the ability to collaborate effectively in a distributed international environment.

Develop data-driven algorithms using raw, noisy, and unlabeled data to improve browser and device identification capabilities. Design and implement supervised, semi-supervised, and unsupervised machine learning approaches, including methods for high-cardinality categorical data. Own data science initiatives end to end, from problem definition and experimentation through production deployment and integration with real-time services. Design experiments and technical solutions for real-time inference, model-to-service integration, training automation, and other machine learning engineering challenges. Conduct exploratory data analysis to investigate business and technical questions, identify anomalies, and evaluate model and dataset performance. Develop practical approaches for collecting and evaluating data when labeled datasets are limited or unavailable. Contribute to an engineering-focused, data-driven culture by sharing tools, methodologies, and effective data science practices with colleagues. Collaborate across data science and engineering functions to turn machine learning concepts into reliable, production-ready services. Participate in a shared on-call rotation, with schedules communicated in advance and coverage designed to minimize disruption outside normal working hours.