Senior Product Analyst (CX)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Analyst (CX) based in Saudi Arabia. This role sits at the heart of product decision-making, using data to uncover opportunities, validate hypotheses, and drive measurable growth. You will take ownership of the product metrics framework and ensure teams have clear, reliable definitions for the numbers that matter most. Working closely with Product and functional leaders, you will investigate anomalies, identify bottlenecks, and translate complex data into actionable insights. You will play a key role in designing and analyzing A/B tests, from experiment setup and statistical validation through recommendations. The position combines rigorous quantitative analysis with strong business and product judgment in a highly collaborative, distributed environment. Your work will directly influence prioritization and help teams focus on opportunities with the greatest potential impact. This is an excellent opportunity for an analytical professional who enjoys turning data into strategic product decisions. Accountabilities • Own and continuously evolve the product metrics tree, ensuring that key metrics have clear, consistent, and locked definitions and that each team can understand how its work influences the overall north-star metric. • Validate existing metrics and dashboards, identify inconsistencies, and create clear specifications for Data Engineering to ensure reliable and accurate reporting. • Partner directly with functional and product leads to investigate fluctuations, anomalies, and unexpected changes in business and product performance. • Conduct ad-hoc research to identify bottlenecks, conversion drop-offs, meaningful customer segments, and non-obvious patterns that may reveal growth opportunities. • Validate product hypotheses through rigorous quantitative analysis and collaborate with Product teams to prioritize problems according to their potential impact on key business metrics. • Design and analyze A/B tests, including sample-size calculations, minimum detectable effect, experiment duration, randomization splits, and pre-test validation. • Verify experiment split correctness, perform sanity checks before interpreting results, and report outcomes alongside appropriate guardrail metrics. • Define data-tracking requirements for experiments, including the events and instrumentation needed to measure results accurately. • Maintain an organized archive of experiments and ensure every test produces clear, actionable recommendations for future product decisions. • Proactively identify data-quality issues and inconsistencies before unreliable information is used to make important product or business decisions. Requirements • A higher education degree in mathematics, statistics, computer science, engineering, or another relevant technical discipline. • Strong knowledge of mathematical statistics and probability theory, with proven experience applying statistical methods to real-world analytical and product problems. • Advanced proficiency in data extraction, processing, analysis, and visualization, with hands-on experience using MS SQL, Python, and Excel. • Deep practical experience designing, running, and analyzing A/B tests, including statistical evaluation and interpretation of experiment results. • Strong understanding of marketing and product metrics, with experience applying them to evaluate performance, identify opportunities, and support strategic decisions. • Strong analytical and problem-solving abilities, with the capacity to investigate complex questions, identify meaningful patterns, and translate findings into practical recommendations. • Excellent communication and collaboration skills, with the ability to work effectively with Product, Data Engineering, and functional leadership teams. • A proactive, detail-oriented mindset and a strong sense of ownership, particularly when it comes to data quality, metric consistency, and the reliability of insights used for decision-making. Benefits • Fully remote, full-time work opportunity. • Competitive compensation, with the initial pay level or salary range shared with candidates during the recruitment process and before employment begins. • 28 calendar days of paid vacation per year. • 7 wellness days per year that can be used for personal matters, recovery, or time away from work without requiring sick leave. • Referral bonuses of up to $5,000 for successfully recommending candidates who are hired. • 50% reimbursement for professional training, international conferences, and professional meetings. • Corporate discounts for English language lessons. • Health benefits, including reimbursement of up to $1,000 gross per year per employee when corporate medical insurance is not available, which can be used toward private health insurance or eligible medical expenses for the employee and close relatives. • Workplace support, including equipment and an equipped workspace where applicable, or reimbursement of up to $1,000 gross once every three years toward coworking costs or home-office setup expenses such as a desk, chair, or internet. • An internal gamified recognition system that allows employees to receive rewards from colleagues and exchange them for merchandise, team-building activities, massage certificates, and other benefits. • A flexible, international remote environment designed to support collaboration across different locations and working styles. 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

A higher education degree in mathematics, statistics, computer science, engineering, or another relevant technical discipline. Strong knowledge of mathematical statistics and probability theory, with proven experience applying statistical methods to real-world analytical and product problems. Advanced proficiency in data extraction, processing, analysis, and visualization, with hands-on experience using MS SQL, Python, and Excel. Deep practical experience designing, running, and analyzing A/B tests, including statistical evaluation and interpretation of experiment results. Strong understanding of marketing and product metrics, with experience applying them to evaluate performance, identify opportunities, and support strategic decisions. Strong analytical and problem-solving abilities, with the capacity to investigate complex questions, identify meaningful patterns, and translate findings into practical recommendations. Excellent communication and collaboration skills, with the ability to work effectively with Product, Data Engineering, and functional leadership teams. A proactive, detail-oriented mindset and a strong sense of ownership, particularly when it comes to data quality, metric consistency, and the reliability of insights used for decision-making.

Own and continuously evolve the product metrics tree, ensuring that key metrics have clear, consistent, and locked definitions and that each team can understand how its work influences the overall north-star metric. Validate existing metrics and dashboards, identify inconsistencies, and create clear specifications for Data Engineering to ensure reliable and accurate reporting. Partner directly with functional and product leads to investigate fluctuations, anomalies, and unexpected changes in business and product performance. Conduct ad-hoc research to identify bottlenecks, conversion drop-offs, meaningful customer segments, and non-obvious patterns that may reveal growth opportunities. Validate product hypotheses through rigorous quantitative analysis and collaborate with Product teams to prioritize problems according to their potential impact on key business metrics. Design and analyze A/B tests, including sample-size calculations, minimum detectable effect, experiment duration, randomization splits, and pre-test validation. Verify experiment split correctness, perform sanity checks before interpreting results, and report outcomes alongside appropriate guardrail metrics. Define data-tracking requirements for experiments, including the events and instrumentation needed to measure results accurately. Maintain an organized archive of experiments and ensure every test produces clear, actionable recommendations for future product decisions. Proactively identify data-quality issues and inconsistencies before unreliable information is used to make important product or business decisions.