12 Biostatistics jobs in the United Arab Emirates

Biostatistics Assistant in Abu Dhabi with 1+ year experience. Skills: data analysis, R, Python. Degree in Statistics or related. Monday-Friday, government client.

Job Code: 4203
Job Title: Biostatistics Assistant
Location: Abu Dhabi
Experience: 1+ years
Client: Government Entity (Direct hire)

Academic Background:
A degree in Statistics, Data Science, Biostatistics, or a related field.

Experience:
1 year of experience in data analysis. Hands-on experience with analysing ecological or biological datasets is highly desirable.

Technical Skills:
Capable of handling, cleaning, and organizing large datasets for statistical analysis.
Knowledge of experimental design, hypothesis testing, and reporting results in a scientific context.
Strong expertise in R and Python for statistical modelling, machine and deep learning, and data visualization.

Soft Skills:
Excellent problem-solving skills with attention to details and accuracy.
Strong organizational and time management skills, with the ability to work both independently and collaboratively.
Strong communication skills, capable of explaining complex ideas clearly and effectively.
Collaborative team player with a positive attitude and commitment to creating a supportive work environment.
Quick learner, adaptable to emerging tools and methodologies in data science and conservation technology.

IT & Software Skills:
Programming Languages: Proficiency in R and Python. Familiarity with SQL and Power BI is an advantage.

Working days and timing: Monday to Friday. 9 hours per day (8 working hours, 1 hour lunch break).

Biostatistics Assistant in Abu Dhabi. Requires 1+ years in data analysis, R/Python skills, and ecological dataset experience. Monday-Friday, 9 hours/day. Government entity direct hire.

Job Code: 4203

Experience: 1+ years

Client: Government Entity (Direct hire)

Academic Background:
A degree in Statistics, Data Science, Biostatistics, or a related field.

Experience:
1 year of experience in data analysis. Hands-on experience with analyzing ecological or biological datasets is highly desirable.

Technical Skills:
Capable of handling, cleaning, and organizing large datasets for statistical analysis.
Knowledge of experimental design, hypothesis testing, and reporting results in a scientific context.
Strong expertise in R and Python for statistical modelling, machine and deep learning, and data visualization.

Soft Skills:
Excellent problem-solving skills with attention to detail and accuracy.
Strong organizational and time management skills, with the ability to work both independently and collaboratively.
Strong communication skills, capable of explaining complex ideas clearly and effectively.
Collaborative team player with a positive attitude and commitment to creating a supportive work environment.
Quick learner, adaptable to emerging tools and methodologies in data science and conservation technology.

IT & Software Skills:
Programming Languages: Proficiency in R and Python.
Familiarity with SQL and Power BI is an advantage.

Working days and timing: Monday to Friday. 9 hours per day (8 working hours, 1 hour lunch break).

Data Reporting and Analytics Consultant III Biostatistics

Posted 1 week ago

Full time

Dubai, AE

Senior

Salary Range: To be discussed

Summary

The Data Reporting and Analytics Consultant at KP-DOR manages and analyzes genomic and EHR data, requiring SAS/SQL skills and experience with large datasets, focusing on epidemiology research.

Description

The Data Reporting and Analytics Consultant will work in the Research Program on Genes, Environment and Health (RPGEH) at the Kaiser Permanente Division of Research (KP-DOR).

RPGEH scientists at KP-DOR conduct NIH-funded epidemiological research in conjunction with the Kaiser Permanente Research Bank (KPRB), a biobank with 400,000 KP adult participants with linked genomic, EHR, and survey data.

RPGEH also facilitates access to the KPRB by scientists outside KP through the provision of preliminary data and de-identified data to access-approved researchers.

RPGEH scientists focus on a broad range of research topics, including pharmacogenetics, genetic and nongenetic factors affecting psychiatric and neurological disorders, vision disorders, cancers and cancer risk factors, cardiovascular disease and risk factors, and other conditions.

The incumbent will work with RPGEH and other investigators on analytical projects using EHR and genomic data and will be required to prepare preliminary data and de-identified analytical datasets for research projects.

Additional duties involve organizing, managing, and documenting the data resources of the program. Candidates are expected to have a working proficiency in SAS and SQL environments .

Prior experience working on Unix-like environments and HPC systems, as well as familiarity with shell scripts and R/Python environments will be beneficial.

Preference will be given to candidates with prior experience handling large scale datasets, particularly genomic data from whole genome SNP microarrays, exome, and whole genome sequence data.

Preference will also be given to candidates with background and training in biostatistics and epidemiology .

The role requires pursuing self-development and effective relationships with others by proactively providing resources, information, advice, and expertise with coworkers and customers; influencing others through technical explanations and examples; providing occasional mentoring to team members; listening and responding to, seeking, and addressing performance feedback; creating plans to capitalize on strengths and develop weaknesses; anticipating and responding to the needs of others; and adapting to and learning from change, difficulties, and feedback.

The consultant will complete work assignments by applying up-to-date expertise in the subject area to generate creative solutions; ensuring all procedures and policies are followed; leveraging an understanding of data and resources to support projects or initiatives; collaborating cross-functionally to solve business problems; identifying and monitoring priorities, deadlines, and expectations; communicating progress and information; identifying, recommending, and implementing ways to address improvement opportunities; and escalating issues or risks as appropriate.

The consultant will support and develop analytical and/or statistical models enabling informed business decisions by determining data and analytical requirements; creating models leading to actionable insights; and testing, refining, and validating models.

The role will also involve gathering data and information on targeted variables in an established systematic fashion by validating data sources; querying, merging, and extracting data across sources; completing routine data refresh and update; developing and/or delivering tools for electronic data collection; and providing user training, support, and documentation.

Additionally, the consultant will support data analysis interpretation by applying findings to contextual settings under the guidance of more senior employees; and developing reports and presentations telling a compelling story to stakeholders to enable and influence decision making.

Finally, the consultant will develop, implement, and automate business and reporting solutions by working with stakeholders in their design, planning, and implementation while ensuring consistency and coherency; summarizing data and results; and creating summary statistics.

Data Reporting and Analytics Consultant III Biostatistics

Posted 1 week ago

Full time

Dubai, AE

Senior

Salary Range: To be discussed

Summary

The Data Reporting and Analytics Consultant at KP-DOR manages and analyzes genomic and EHR data, requiring SAS/SQL skills and experience with large datasets, focusing on epidemiology research.

Description

The Data Reporting and Analytics Consultant will work in the Research Program on Genes, Environment and Health (RPGEH) at the Kaiser Permanente Division of Research (KP-DOR).

RPGEH scientists at KP-DOR conduct NIH-funded epidemiological research in conjunction with the Kaiser Permanente Research Bank (KPRB), a biobank with 400,000 KP adult participants with linked genomic, EHR, and survey data.

RPGEH also facilitates access to the KPRB by scientists outside KP through the provision of preliminary data and de-identified data to access-approved researchers.

RPGEH scientists focus on a broad range of research topics, including pharmacogenetics, genetic and nongenetic factors affecting psychiatric and neurological disorders, vision disorders, cancers and cancer risk factors, cardiovascular disease and risk factors, and other conditions.

The incumbent will work with RPGEH and other investigators on analytical projects using EHR and genomic data and will be required to prepare preliminary data and de-identified analytical datasets for research projects.

Additional duties involve organizing, managing, and documenting the data resources of the program. Candidates are expected to have a working proficiency in SAS and SQL environments.

Prior experience working on Unix-like environments and HPC systems, as well as familiarity with shell scripts and R/Python environments will be beneficial.

Preference will be given to candidates with prior experience handling large scale datasets, particularly genomic data from whole genome SNP microarrays, exome, and whole genome sequence data.

Preference will also be given to candidates with background and training in biostatistics and epidemiology.

The role requires pursuing self-development and effective relationships with others by proactively providing resources, information, advice, and expertise with coworkers and customers; influencing others through technical explanations and examples; providing occasional mentoring to team members; listening and responding to, seeking, and addressing performance feedback; creating plans to capitalize on strengths and develop weaknesses; anticipating and responding to the needs of others; and adapting to and learning from change, difficulties, and feedback.

The consultant will complete work assignments by applying up-to-date expertise in the subject area to generate creative solutions; ensuring all procedures and policies are followed; leveraging an understanding of data and resources to support projects or initiatives; collaborating cross-functionally to solve business problems; identifying and monitoring priorities, deadlines, and expectations; communicating progress and information; identifying, recommending, and implementing ways to address improvement opportunities; and escalating issues or risks as appropriate.

The consultant will support and develop analytical and/or statistical models enabling informed business decisions by determining data and analytical requirements; creating models leading to actionable insights; and testing, refining, and validating models.

The role will also involve gathering data and information on targeted variables in an established systematic fashion by validating data sources; querying, merging, and extracting data across sources; completing routine data refresh and update; developing and/or delivering tools for electronic data collection; and providing user training, support, and documentation.

Additionally, the consultant will support data analysis interpretation by applying findings to contextual settings under the guidance of more senior employees; and developing reports and presentations telling a compelling story to stakeholders to enable and influence decision making.

Finally, the consultant will develop, implement, and automate business and reporting solutions by working with stakeholders in their design, planning, and implementation while ensuring consistency and coherency; summarizing data and results; and creating summary statistics.

This role supports strategic and operational decision-making across departments. It combines strong business acumen with technical proficiency in tools like Excel, SQL, Python, and Power BI.

• Data collection, cleaning, and analysis from various systems.
• Maintenance of regular performance dashboards for occupancy, revenue, collections, and expenses.
• Generation of actionable insights and data visualizations to support leadership in key decisions.
• Assistance with forecasting and scenario planning (e.g., revenue trends, cost optimization).
• Ad hoc analysis and research on market trends, competitors, and benchmarks.
• Collaboration with teams (Finance, Legal, IT, etc.) to understand business needs and validate data.
• Identification of inconsistencies or inefficiencies in data or internal workflows, and proposal of improvements.
• Support for automation of recurring reports using Excel, BI tools, SQL queries, or Python scripts.

• Bachelor's degree in a STEM field, Finance, Economics, Statistics, Business Analytics, or a related discipline.
• 2-4 years of experience in a business or data analysis role, preferably within real estate or operational functions.
• Familiarity with UAE real estate regulations is an advantage.
• Proficiency in Microsoft Excel (advanced level), SQL, and Python for data handling and automation.
• Experience with BI tools such as Power BI (or similar platforms).
• Ability to build dashboards, analyze KPIs, and explain findings to non-technical stakeholders.

• Full-time employment offers the opportunity to work with a dynamic team and contribute to the company's success.
• Employment Type: Full-time.
• Job Function: Business Development and Investment Management.

The Director of Data Analysis is responsible for collecting, processing, and analysing real estate data from various sources, with the aim of providing accurate, data-driven insights that support strategic decision-making in the real estate sector in United Arab Emirates. The role focuses on enhancing market transparency, developing sector-wide performance indicators, and supporting policy formulation and investment planning based on data.

Responsibilities

• Collecting and analyzing real estate data from multiple sources
• Processing and cleaning data to ensure its accuracy, consistency, and readiness for analysis
• Analyzing real estate data to extract and update the real estate index, which enhances market transparency
• Preparing reports and dashboards to support strategic decision-making
• Developing sector performance indicators (e.g., price indices, supply and demand, occupancy rates, etc.)
• Supporting policy development and investment planning by providing data-driven recommendations
• Contributing to real estate market studies and identifying market trends to support and update strategic urban development plans
• Collaborating with government entities and investors to provide transparent and accurate insights into the real estate market

Requirements

• Bachelor's degree in Economics, Statistics, Business Administration, Data Analysis, or a related field.
• 5 years of experience in data analysis or market research, with knowledge of the real estate market.
• Certifications in data analysis or economics are not required but are preferred
• Certifications in Data Analysis, such as RICS or CF

We are seeking a skilled professional to lead our data analysis team. As a Lead Data Analyst, you will be responsible for overseeing a team of analysts and collaborating with developers and stakeholders to ensure software meets business needs.

• Manage a team of data analysts
• Create data marts and data lakes using mappings
• Design user interfaces and dashboards for data visualization and interaction
• Test and validate software and data products for accuracy and reliability

• Bachelor's or master's degree in Computer Science, IT, or related field
• Experience with data processing and analysis using Data Bricks
• Expert-level proficiency in SQL, Python, SAS/R, Spark
• Deployment experience in databases, server/cloud environments (AWS, Azure), APIs, ODBCs, web apps

• Career Growth: Opportunities for professional development and advancement
• Performance-Based Compensation: Competitive pay linked to performance
• Inclusive Culture: Join a collaborative team that values innovation and every voice

This is an exciting opportunity to work with a talented team and contribute to our success.

Effective Business Decisions Using Data Analysis

ID 257

Course: Effective Business Decisions Using Data Analysis

This interactive, applications-driven 5-day course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.

• Discussions on applications of data analytics in management
• The importance of data in data analytics
• Applying data analytical methods through worked examples
• Focusing on management interpretation of statistical evidence
• How to integrate statistical thinking into the work domain

• Explain the scope and structure of data analytics.
• Apply a cross-section of useful data analytics.
• Interpret meaningfully and critically assess statistical evidence.
• Identify relevant applications of data analytics in practice.

• Professionals in management support roles
• Analysts who typically encounter data/analytical information regularly in their work environment
• Those who seek to derive greater decision-making value from data analytics

This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. The daily workshops will be highly interactive and participative. This involves regular discussion of applications as well as hands-on exposure to data analytics techniques using Microsoft Excel. Delegates are strongly encouraged to bring and analyse data from their own work domain. This adds greater relevancy to the content. Emphasis is also placed on the valid interpretation of statistical evidence in a management context.

• Day One: Setting the Statistical Scene in Management
  • Introduction; The quantitative landscape in management
  • Thinking statistically about applications in management (identifying KPIs)
  • The integrative elements of data analytics
  • Data: The raw material of data analytics (types, quality, and data preparation)
  • Exploratory data analysis using Excel (pivot tables)
  • Using summary tables and visual displays to profile sample data
• Day Two: Evidence-based Observational Decision Making
  • Numeric descriptors to profile numeric sample data
  • Central and non-central location measures
  • Quantifying dispersion in sample data
  • Examine the distribution of numeric measures (skewness and bimodal)
  • Exploring relationships between numeric descriptors
  • Breakdown analysis of numeric measures
• Day Three: Statistical Decision Making – Drawing Inferences from Sample Data
  • The foundations of statistical inference
  • Quantifying uncertainty in data – the normal probability distribution
  • The importance of sampling in inferential analysis
  • Sampling methods (random-based sampling techniques)
  • Understanding the sampling distribution concept
  • Confidence interval estimation
• Day Four: Statistical Decision Making – Drawing Inferences from Hypotheses Testing
  • The rationale of hypotheses testing
  • The hypothesis testing process and types of errors
  • Single population tests (tests for a single mean)
  • Two independent population tests of means
  • Matched pairs test scenarios
  • Comparing means across multiple populations
• Day Five: Predictive Decision Making - Statistical Modeling and Data Mining
  • Exploiting statistical relationships to build prediction-based models
  • Model building using regression analysis
  • Model building process – the rationale and evaluation of regression models
  • Data mining overview – its evolution
  • Descriptive data mining – applications in management
  • Predictive (goal-directed) data mining – management applications

About the opportunity

The Growth Data Science Director will lead and accelerate growth strategies across marketing optimization, product initiatives (e.g., churn reduction), ecosystem plays, and ads revenue for talabat and Quick commerce. This role requires collaboration with product teams, senior marketing, and partner leadership to drive data-driven decisions and business growth.

What’s On Your Plate?

• Data Science: Define and execute data strategies aligned with company goals, leading cross-functional teams to develop innovative data-driven products and insights that impact business outcomes.
• Strategic Leadership and Business Acumen: Demonstrate strategic leadership, articulate vision, and incorporate emerging trends and technologies to add value.
• Collaboration and Influence: Work with stakeholders including executives, product managers, engineers, and external partners; represent the organization at industry events.
• Resource Management: Manage teams, budgets, and resources; prioritize projects; coach and mentor managers.
• Strategic Hiring and Talent Development: Recruit, develop career paths, and provide feedback to team members.
• Organizational Change Management: Lead change initiatives, communicate effectively, and manage resistance.

What you need to be successful

• 8-10+ years in data science, with leadership experience in managing teams or consulting.
• Ph.D. or Master’s in relevant fields such as computer science, statistics, or data science.
• Proven leadership in fostering high-performance teams and applying data science skills to create impactful products and insights.
• Deep knowledge of statistics, causality, experimentation, and modeling.
• Business acumen with experience in quantifying impact through data initiatives.
• Strategic thinking and ability to develop data-driven roadmaps aligned with organizational goals.

Who we are

Since 2004, talabat has been Kuwait’s leading on-demand food and Q-commerce app, serving eight countries. We leverage technology to simplify life, optimize operations, and provide earning opportunities. We foster a high-performance culture, value authenticity, and are proud of our awards and diverse team of over 6,000 Talabaty committed to making a difference.