86 Tableau jobs in Dubai

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.

Job Title: Data Expert

At our organization, we are seeking a talented Data Expert to join our team. As a key member of our data science team, you will play a vital role in helping us make informed decisions by leveraging data insights and analysis.

Key Responsibilities:

• Design and implement advanced statistical models to analyze complex data sets.
• Develop and maintain databases to store and manage large datasets.
• Collaborate with cross-functional teams to identify business needs and develop solutions.
• Communicate findings and insights effectively to both technical and non-technical stakeholders through reports, presentations, and visualizations.
• Stay up-to-date with the latest advancements in data science, machine learning, and AI technologies.

Required Skills and Qualifications:

• Bachelor's degree in statistics, applied mathematics, or related discipline.
• Minimum of 4-6 years of experience in a similar role.
• Proficiency with data mining, mathematics, and statistical analysis.
• Advanced pattern recognition and predictive modeling experience.
• Experience with Excel, Tableau/Looker Studio, SQL, and programming languages such as Python and R.
• Storytelling and data visualization skills.
• Comfort working in a dynamic, data-oriented team with several ongoing concurrent projects.

Preferred Qualifications:

• Master's degree in stats, applied math, or related discipline.
• Experience with NoSQL, Pig, Hive, and PySpark/Big Query.

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

• Bachelors 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 highly skilled Data Analyst to join our team in Dubai. As a key member of our organization, you will be responsible for collecting, organizing, and analyzing data that informs business decisions.

- Collect and analyze large datasets to identify trends and patterns
- Develop and maintain databases to ensure accurate data storage and retrieval
- Create data visualizations to effectively communicate insights to stakeholders
- Collaborate with cross-functional teams to drive business growth through data-driven decision making

- Bachelor's degree in Statistics, Mathematics, Computer Science, or related field
- Proficient in database management systems such as SQL and programming languages like Python
- Excellent problem-solving skills and ability to interpret complex data quickly and accurately
- Strong communication and presentation skills, with ability to clearly convey findings to colleagues and stakeholders
- Ability to work independently and collaboratively as part of a team

- Salary of 1300 AED per month
- Opportunity to work in a dynamic and growing industry
- Chance to develop your skills and expertise in data analysis

This is an excellent opportunity for individuals who are passionate about data and its applications. If you are motivated, detail-oriented, and able to work well under pressure, we encourage you to apply.

We are seeking a skilled professional to transform complex construction data into actionable insights. If you have a passion for data analysis and experience in the infrastructure or construction sector, this role is ideal for you.

The Reporting Specialist will design and build interactive dashboards and reports using Power BI and other Microsoft Office tools (Excel, Access, etc.). The successful candidate will collect, clean, and organize data from different sources to ensure accuracy and reliability. They will develop data models and calculations to support reporting needs, provide regular and ad-hoc reports to management and project teams, and support users with training and troubleshooting for dashboards and reports. Additionally, they will document data sources, reporting processes, and user guides as needed.

• Bachelor Degree/Diploma or proven experience in data analysis or reporting (any industry, construction/infrastructure preferred)
• Advanced Power BI skills: experience with Power Query and Power Automate is an advantage
• Proficiency in Microsoft Excel and other Office applications
• Previous experience developing reports or dashboards for business use
• Good understanding of project management, construction, or infrastructure sector preferred
• Strong communication skills; ability to explain data to non-technical users

• Opportunity to work with complex construction data and transform it into actionable insights
• Chance to develop and improve your data analysis and reporting skills
• Collaborative and dynamic work environment
• Ongoing training and support to enhance your skills

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

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

This 5-day course focuses on leveraging data analytics as a decision support tool in management. Data analytics is increasingly being used by professionals to make informed business decisions.

• Explore the applications of data analytics in management practice
• Leverage statistical evidence and integrate quantitative reasoning into decision making
• Promote confidence in using evidence-based information

• Explain the scope and structure of data analytics
• Apply cross-sectional data analytics techniques
• Interpret and critically assess statistical evidence
• Identify relevant applications of data analytics in practice

• Management support professionals
• Data analysts regularly encountering data/analytical information
• Professionals seeking to derive greater decision-making value from data analytics

Key Skills:

• Ability to interpret statistical evidence
• Integration of quantitative reasoning into decision making
• Appreciation of data analytics applications in management

Benefits:

• Improved decision-making skills through data-driven insights
• Enhanced understanding of data analytics in management
• Increased confidence in using evidence-based information