Data Scientist

**We all know every decision should be driven by data. But what about the data you don’t know? For years, the status quo in data aggregation has lacked visibility, moved slowly, and cost too much. Leaving organizations to make critical decisions, day after day, without the whole picture. Premise changes that.**

Across 138 countries and counting, our technology connects communities of global smartphone users to source actionable data in real-time, cost
- effectively, and with the visibility you need. So leaders inside organizations, non-profit agencies and governments can now make the best decisions under the best conditions.

With Premise, organizations win. And communities win, too. People can earn more from their opinions and discoveries. They can influence their cities for the better. And, unlike other data sourcing methods out there, they can do it all with full transparency that the data they’re gathering is going to an organization that values it, and values them.

**Position Summary**

We’re looking for an experienced** Data Scientist** to play a key role in driving data quality excellence and analytical insights within Premise’s Market Performance Operations team. In this role, you will ensure the accuracy, consistency, and timely delivery of high-quality data, working closely with cross-functional teams to improve our processes and data products. Your efforts will directly influence the quality of Premise’s market research studies and contribute to data-driven decision-making across the organization.

As a key part of the team, you’ll not only oversee data quality but also drive improvements in how we govern, manage, transform, and deliver data. If you are passionate about data and ready to shape the future of data operations, we want to hear from you.

**What You Bring Along**
- Proven experience in managing data quality, data transformation, and data completeness processes.
- Expertise with data pipelines, ETL processes, and integration tools (like DataForm/DBTs, SQL, Python, etc.).
- Strong problem-solving skills with the ability to identify and resolve complex data issues.
- Experience with standardizing data schemas, maintaining consistency across data sources and use of best practice techniques.
- Knowledge of data automation and model development, particularly in large-scale environments.
- Excellent collaboration skills, with the ability to work across cross-functional teams to drive results.

**What You Will Do**

Data Quality Management (DQM) for Commercial Products
- Develop and document a comprehensive Data Quality Management (DQM) process for commercial products, focusing on ensuring consistency, accuracy, and transparency across data operations.
- Update the Data Governance approach, including the documentation of data governance rules and ownership. Clearly outline roles and responsibilities across teams to ensure adherence to data quality standards.
- Establish a transparent ruleset for what data will be improved and what will not, providing clarity to all stakeholders on the scope of improvement efforts based on impact, feasibility, and business priorities.
- Work with cross-functional teams to ensure alignment on the DQM process and its ongoing execution across the organization.
- Define and track key Data Quality Metrics to evaluate and report on data quality performance. Establish targets for each metric and create systems for continuous monitoring and improvement.

Data Quality Oversight
- Ensure stable, reliable, and accurate data production across all countries in which MPS is active.
- Lead efforts to improve data accuracy and reduce delivery timeframes across teams.
- Develop and manage frameworks for measuring quality escapes and set reduction targets.
- Ensure on-time delivery of high-quality market research studies.
- Standardize and optimize quality processes to drive consistent results.
- Collaborate with operations teams to implement strategies for continuously improving data quality.
- Provide actionable recommendations for both short
- and long-term improvements to data quality.

Alert Prioritization & Proactive Investigation
- Develop and implement a framework to prioritize existing data quality alerts based on severity and impact.
- Create a system to surface critical alerts that require proactive investigation and immediate resolution.
- Collaborate with Customer Success, Data Delivery, and Operations teams to define key metrics for evaluating alert severity.
- Drive continuous improvement in alert handling processes to ensure high-priority issues are addressed promptly.

Root Cause Analysis of Data Anomalies
- Develop a framework to conduct root cause analysis to identify the underlying drivers of data quality issues.
- Collaborate with cross-functional teams to pinpoint systemic issues and design solutions to prevent recurrence.
- Develop and implement corrective actions based on insights from root cause analysis to improve data production processes.
- Identif