Senior Data Scientist – Data Architecture & Governance

Senior Data Scientist – Data Architecture & Governance

Automations · Full-time · In-Person (Islamabad, PK) · Hours 6pm - 2am PKT

• About Nysonian
• Nysonian builds the next generation of global lifestyle brands, shaping how people travel, move, and live. We go beyond creating great products to build experiences that elevate everyday life and empower people around the world.

Our Fast-Growing Portfolio Includes:

NOBL Travel — redefining modern travel with design, durability, and performance

FLO Pilates — bringing Pilates into homes and wardrobes globally

With $350M+ in revenue, 400+ teammates across 8 countries, and 1M+ customers worldwide, we are shaping the brands that will define the next decade.

Core Values: Winners’ Mindset | Speed with Purpose | Thoughtful Innovation | Genuineness | No Ego, Full Ownership

• The Opportunity
• We are looking for an experienced Senior Data Scientist to join and strengthen our existing data environment. The individual will work across multiple business verticals, including Operations, Finance, Marketing, Sales, Customer Support, eCommerce, and Travel Technology.

PostgreSQL is our primary and central database. Our wider technology environment also includes MongoDB, Supabase, Firebase, and AWS-hosted infrastructure.

The successful candidate will be responsible for understanding the existing architecture, improving how data is structured and exchanged between systems, and establishing company-wide protocols for data collection, storage, transformation, validation, access, security, and reporting.

This is an ownership-heavy position suited to someone who can operate effectively in a fast-paced, high-pressure environment. The individual must balance urgent business requirements with the long-term reliability, security, and scalability of the company’s data infrastructure.

What You'll Own

PostgreSQL and Data Architecture

Take ownership of the structure, integrity, and optimization of the company’s primary PostgreSQL database.

Assess and document the existing architecture, database schemas, integrations, dependencies, and data flows.

Design scalable PostgreSQL schemas, tables, views, materialized views, indexes, and analytical data models.

Review and improve database performance through query optimization, indexing, partitioning, and appropriate data-modelling practices.

Establish PostgreSQL as the governed source of truth for approved business entities and metrics.

Define clear boundaries between PostgreSQL, MongoDB, Supabase, Firebase, and other connected systems.

Reduce unnecessary duplication, fragmented datasets, and conflicting versions of business information.

Create safe procedures for schema changes, database migrations, version control, testing, and deployment.

Work with Engineering and DevOps to maintain database availability, monitoring, backups, recovery procedures, and performance.

Ensure that new applications and features follow established data architecture standards.

Data Pipelines and System Integration

Design, build, and maintain reliable ETL/ELT pipelines between PostgreSQL and internal or third-party systems.

Integrate data from MongoDB, Supabase, Firebase, APIs, eCommerce platforms, operational tools, and other business applications.

Treat Firebase as a supporting source for application and event data, with validated information transferred into the central data environment where required.

Develop standardized processes for data extraction, transformation, synchronization, reconciliation, and loading.

Implement incremental data-processing and change-tracking methods where appropriate.

Monitor pipeline failures, delayed data, schema changes, duplication, and synchronization issues.

Ensure integrations are scalable, documented, testable, and recoverable.

Design reusable datasets that can support reporting, business intelligence, automation, experimentation, and machine-learning use cases.

Data Governance and Management Protocols

Create and implement a company-wide data-management framework covering the complete data lifecycle.

Establish standards for data collection, naming conventions, schema design, ownership, retention, transformation, access, and archival.

Define clear data ownership across Finance, Operations, Marketing, Sales, Customer Support, Product, and other business verticals.

Establish a controlled process for requesting new fields, tables, datasets, integrations, and reporting metrics.

Develop data dictionaries, lineage documentation, architecture diagrams, system maps, and source-of-truth registers.

Create formal procedures for schema changes, pipeline changes, metric definitions, access requests, data incidents, and recovery.

Define validation, reconciliation, and approval processes for business-critical information.

Establish role-based access controls in collaboration with Engineering, DevOps, and Information Security.

Ensure that sensitive information is collected, stored, and accessed appropriately.

Monitor compliance with established data protocols and address violations or weaknesses.

Analytics and Data Science

Translate business problems into clear analytical questions, measurable outcomes, and technical requirements.

Conduct exploratory, diagnostic, predictive, and prescriptive analysis.

Develop forecasting, segmentation, anomaly-detection, and optimization models where they create measurable value.

Support analysis across revenue, inventory, demand, customer behavior, subscriptions, fulfillment, marketing, and operational performance.

Create governed analytical datasets using PostgreSQL as the primary foundation.

Establish consistent definitions for KPIs and business metrics across departments.

Ensure that models and analyses are explainable, reproducible, validated, and properly documented.

Communicate the assumptions, limitations, and confidence levels associated with analytical outputs.

Support the development of production-ready machine-learning and AI capabilities where appropriate.

Data Quality and Reliability

Build automated checks for data completeness, accuracy, consistency, validity, freshness, and duplication.

Define data-quality thresholds and service-level expectations for critical datasets and pipelines.

Identify and resolve inconsistencies between PostgreSQL, connected systems, and departmental reports.

Implement reconciliation controls for financial, operational, inventory, customer, and revenue data.

Lead root-cause analysis for data incidents and implement permanent corrective measures.

Develop monitoring and alerting for pipeline failures, abnormal values, missing records, delayed data, and schema changes.

Ensure dashboards and business reports use governed, validated, and reconciled datasets.

Cross-Functional Leadership

Collaborate with Internal Systems, Engineering, DevOps, Finance, Operations, Marketing, Sales, Product, and executive leadership.

Convert loosely defined business requirements into clear technical specifications and data models.

Challenge unreliable assumptions, inconsistent definitions, and unsupported conclusions.

Present technical findings in language that business stakeholders can understand and act upon.

Train teams on data definitions, governance requirements, and responsible data usage.

Manage competing priorities without compromising the integrity of critical systems.

Communicate clearly during high-pressure incidents and time-sensitive projects.

Skills & Qualifications

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.

At least five years of professional experience in data science, analytics engineering, data engineering, or data architecture.

Advanced proficiency in PostgreSQL and SQL.

Strong proficiency in Python for data processing, analysis, automation, and modelling.