Description review
Data Scientist - Remote
Supersub · India · back to the listing
HR standards
62/100
needs work
Title ↔ description
86/100
strong
Reads as
Data Scientist
99% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Data Scientist - Algorithms, Community Support Airbnb
- Data Scientist - Inference, Community Support Airbnb
- Senior Data Scientist, Guest & Host Airbnb
- Senior Data Scientist, Trust (Inference) Airbnb
- Data Scientist, Analytics (Technical Leadership) Meta
Startups
- Amplify Renewables | Quantitative Researcher | SF / Seattle / US | Hybrid (Remote Possible) | Full-Time | $200k - $375k (all cash) Amplify Renewables
- Senior Data Scientist BoldVoice
- Senior Research Neuroscientist ANORIA
- Quantitative Associate Corgi Insurance
- Senior Data Scientist Coulomb AI
What the listing never says
- 25 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
About the Role:
We seek a skilled Data Scientist to join a Saudi Fintech. This role involves building and maintaining data pipelines, designing and managing data warehouses (DWH), and developing reports and dashboards to drive business decisions. The ideal candidate has experience with data modeling, machine learning, and analytics, ensuring that insights are actionable and aligned with business goals.
Key Responsibilities:
1. Data Management & Engineering
• Design, build, and maintain ETL/ELT data pipelines for collecting, processing, and storing structured and unstructured data.
• Develop and manage the data warehouse (DWH) architecture to ensure scalability and efficiency.
• Integrate and optimize data from multiple sources, including databases, APIs, third-party tools, and business applications.
• Ensure data integrity, consistency, and security across all systems.
2. Business Intelligence & Reporting
• Collaborate with business teams to understand data needs and develop dashboards and reports for key performance indicators (KPIs).
• Use SQL, Python, R, or BI tools (Tableau, Power BI, Looker, etc.) to analyze and visualize data effectively.
• Provide actionable insights to drive business strategies, optimize operations, and improve customer experiences.
3. Data Science & Advanced Analytics
• Apply machine learning and statistical modeling to uncover trends, predict outcomes, and drive strategic decisions.
• Implement A/B testing frameworks and experiments to measure business impact.
• Optimize algorithms for fraud detection, customer segmentation, demand forecasting, and operational efficiency.
4. Cross-functional Collaboration
• Work closely with engineers to optimize data infrastructure and pipelines.
• Partner with business stakeholders to define data-driven strategies and objectives.
• Act as a bridge between technical and non-technical teams, ensuring that analytics solutions align with business needs.
Role Requirements
• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• 5+ years of experience in data science, analytics, or related fields.
• Strong SQL skills and experience working with relational and NoSQL databases.
• Proficiency in Python (Pandas, NumPy, Scikit-Learn, etc.) or R for data analysis and machine learning.
• Hands-on experience with ETL pipelines, data processing, and data warehousing (e.g., Snowflake, Redshift, BigQuery).
• Knowledge of cloud platforms (OCI, GCP, Azure) and experience with data tools like Airflow, DBT, Spark, or Kafka.
• Experience with BI tools (Power BI, Tableau, Looker, Metabase, etc.) for data visualization and reporting.
• Strong understanding of statistics, machine learning algorithms, and predictive modeling.
• Experience in Fin-Tech, banking, or finance is a Plus.
• Familiarity with big data technologies (Hadoop, Spark, Databricks, etc.) is a Plus.
• Knowledge of data governance, compliance, and security best practices is a Plus.
• Experience with real-time analytics and streaming data is a Plus.
Originally posted on Himalayas
We seek a skilled Data Scientist to join a Saudi Fintech. This role involves building and maintaining data pipelines, designing and managing data warehouses (DWH), and developing reports and dashboards to drive business decisions. The ideal candidate has experience with data modeling, machine learning, and analytics, ensuring that insights are actionable and aligned with business goals.
Key Responsibilities:
1. Data Management & Engineering
• Design, build, and maintain ETL/ELT data pipelines for collecting, processing, and storing structured and unstructured data.
• Develop and manage the data warehouse (DWH) architecture to ensure scalability and efficiency.
• Integrate and optimize data from multiple sources, including databases, APIs, third-party tools, and business applications.
• Ensure data integrity, consistency, and security across all systems.
2. Business Intelligence & Reporting
• Collaborate with business teams to understand data needs and develop dashboards and reports for key performance indicators (KPIs).
• Use SQL, Python, R, or BI tools (Tableau, Power BI, Looker, etc.) to analyze and visualize data effectively.
• Provide actionable insights to drive business strategies, optimize operations, and improve customer experiences.
3. Data Science & Advanced Analytics
• Apply machine learning and statistical modeling to uncover trends, predict outcomes, and drive strategic decisions.
• Implement A/B testing frameworks and experiments to measure business impact.
• Optimize algorithms for fraud detection, customer segmentation, demand forecasting, and operational efficiency.
4. Cross-functional Collaboration
• Work closely with engineers to optimize data infrastructure and pipelines.
• Partner with business stakeholders to define data-driven strategies and objectives.
• Act as a bridge between technical and non-technical teams, ensuring that analytics solutions align with business needs.
Role Requirements
• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• 5+ years of experience in data science, analytics, or related fields.
• Strong SQL skills and experience working with relational and NoSQL databases.
• Proficiency in Python (Pandas, NumPy, Scikit-Learn, etc.) or R for data analysis and machine learning.
• Hands-on experience with ETL pipelines, data processing, and data warehousing (e.g., Snowflake, Redshift, BigQuery).
• Knowledge of cloud platforms (OCI, GCP, Azure) and experience with data tools like Airflow, DBT, Spark, or Kafka.
• Experience with BI tools (Power BI, Tableau, Looker, Metabase, etc.) for data visualization and reporting.
• Strong understanding of statistics, machine learning algorithms, and predictive modeling.
• Experience in Fin-Tech, banking, or finance is a Plus.
• Familiarity with big data technologies (Hadoop, Spark, Databricks, etc.) is a Plus.
• Knowledge of data governance, compliance, and security best practices is a Plus.
• Experience with real-time analytics and streaming data is a Plus.
Originally posted on Himalayas