2,697 Senior Research Scientist Data Analytics jobs in Kenya

• Lead the design and implementation of sophisticated statistical models and machine learning algorithms for scientific data analysis.
• Develop and manage data pipelines for ingesting, cleaning, transforming, and analyzing large-scale scientific datasets from various sources (e.g., experimental, observational, genomic).
• Collaborate closely with domain experts (biologists, chemists, physicists, etc.) to understand research questions and translate them into data science problems.
• Mentor and guide a team of data scientists, providing technical leadership and fostering professional development.
• Design and conduct rigorous A/B testing and validation studies to assess the performance of models and hypotheses.
• Develop reproducible research workflows and ensure best practices in coding, version control, and documentation.
• Communicate complex analytical findings and insights effectively to both technical and non-technical audiences through clear visualizations and presentations.
• Identify opportunities to leverage new data science techniques and technologies to solve challenging scientific problems.
• Contribute to grant proposals and publications by providing data analysis expertise and insights.
• Ensure data privacy, security, and ethical considerations are maintained throughout all data science projects.
• Stay current with the latest advancements in data science, machine learning, artificial intelligence, and their applications in scientific research.

• Ph.D. or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• A minimum of 7 years of progressive experience in data science, with a significant portion focused on scientific or research applications.
• Demonstrated experience in leading data science projects and mentoring junior team members.
• Expertise in programming languages such as Python or R, and proficiency with relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
• Strong understanding of statistical modeling, machine learning algorithms, and experimental design.
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP) is highly desirable.
• Excellent data visualization skills and experience with tools like Matplotlib, Seaborn, or Tableau.
• Exceptional analytical, problem-solving, and critical thinking abilities.
• Superb communication and collaboration skills, with the ability to work effectively in a distributed, remote team.
• Experience with bioinformatics, cheminformatics, or other specialized scientific data analysis is a plus.

• Lead the design and implementation of complex data science projects to address critical research questions.
• Develop and apply advanced statistical methods, machine learning algorithms, and predictive models to large, complex scientific datasets.
• Extract, clean, transform, and analyze diverse data sources (e.g., experimental data, simulations, observational data).
• Visualize complex data and communicate findings clearly and effectively to researchers, stakeholders, and collaborators.
• Mentor and guide junior data scientists and research staff in data analysis methodologies.
• Collaborate with principal investigators and research teams to define research objectives and data requirements.
• Stay abreast of the latest advancements in data science, machine learning, artificial intelligence, and their applications in scientific research.
• Develop and maintain robust data pipelines and computational workflows.
• Evaluate and implement new tools and technologies for data analysis and modeling.
• Contribute to grant proposals and research publications by providing data science expertise.
• Ensure the reproducibility and validity of analytical results.
• Identify opportunities to leverage data for novel discoveries and advancements.
• Manage data governance, privacy, and ethical considerations in research data analysis.
• Present research findings at scientific conferences and workshops.
• Troubleshoot and resolve complex analytical challenges.

• Ph.D. in Data Science, Statistics, Computer Science, Physics, Mathematics, or a related quantitative field.
• Minimum of 10 years of experience as a Data Scientist, with a significant focus on scientific research applications.
• Extensive expertise in statistical modeling, machine learning techniques (e.g., deep learning, Bayesian methods, reinforcement learning), and experimental design.
• Proficiency in programming languages such as Python or R, and experience with relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong experience with big data technologies and distributed computing frameworks (e.g., Spark, Hadoop).
• Excellent data visualization skills and experience with tools like Matplotlib, Seaborn, or Tableau.
• Demonstrated ability to lead complex analytical projects and mentor junior team members.
• Exceptional problem-solving, critical-thinking, and analytical skills.
• Outstanding communication and presentation skills, capable of explaining complex technical concepts to both technical and non-technical audiences remotely.
• Ability to work independently, manage priorities, and drive projects to completion in a remote setting.
• Experience in a specific scientific domain (e.g., genomics, astrophysics, bioinformatics, computational chemistry) is highly desirable.
• Strong understanding of data management best practices and database technologies.

• Develop and implement computational pipelines for analyzing large biological datasets.
• Perform statistical analysis and interpretation of multi-omics data (genomics, transcriptomics, etc.).
• Apply machine learning techniques to biological problems.
• Design computational aspects of research studies.
• Collaborate with experimental biologists and clinicians.
• Visualize complex biological data effectively.
• Contribute to manuscript preparation and scientific publications.
• Mentor junior computational biologists and bioinformaticians.
• Manage research projects and timelines.
• Stay current with advancements in bioinformatics and computational biology.

• PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related field.
• Minimum of 7 years of progressive experience in computational biology or bioinformatics research.
• Proficiency in programming languages such as Python and R.
• Experience with bioinformatics tools and databases (e.g., BLAST, Ensembl, TCGA).
• Strong understanding of statistical methods and machine learning algorithms.
• Experience with cloud computing platforms and high-performance computing (HPC) is a plus.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work independently and manage multiple research projects in a remote setting.

Summary
The Senior Research Analyst is responsible for internal project management and report writing. This role requires the ability to effectively communicate with stakeholders, manage multiple projects and analyze complex data sets, synthesize information, and present findings in a clear and concise manner. To be successful in this role, you must have strong communication skills, excellent analytical skills, attention to detail, and the ability to multitask.

Responsibilities
Participate in client meetings to ensure comprehension of client needs.

Support in writing RFPs, questionnaire design, writing research reports, strategic recommendations and client presentation.

Create statistical analysis and reporting plans, conducting analysis of results.

Full cycle project management in collaboration with the operations team to ensure quality control and adherence to project timelines, such as validating the questionnaire's test link, interviewer training, coordinating field implementation, follow-up and validation of the pre-test, daily project field monitoring, project follow-up and coordinating end of fieldwork stages.

Qualifications
Bachelor's or Master's in Sociology, Psychology, Business or related field

Minimum 4 years' work experience in client facing/customer service roles

Minimum with 2 years' Market Research experience as a Market Research Analyst, preferably on supplier side research.

Skills
Strong Project Management skill: Adept managing multiple projects and timelines at once.

Strong Client Management skill: Adept at nurturing client relationships, understanding client needs and helping clients in solving their business problems with research.

Excellent communication and presentation skills: Ability to convey complex data and insights in a clear and compelling manner.

Excellent analytical skills: Ability to examine and interpret data with keen attention to detail; identify trends, patterns, and insights, and draw meaningful conclusions that is useful for decision-making.

• Lead the development and application of advanced statistical and machine learning models for genomic data analysis.
• Design and execute bioinformatics pipelines for processing and analyzing next-generation sequencing (NGS) data.
• Identify and validate genetic variants associated with complex diseases and traits.
• Collaborate with biologists, clinicians, and other researchers to define research questions and hypotheses.
• Develop and implement strategies for data integration from various sources (e.g., clinical, molecular, imaging).
• Mentor junior data scientists and bioinformatics specialists.
• Stay abreast of the latest advancements in genomics, bioinformatics, and data science.
• Present research findings at international conferences and publish in high-impact journals.
• Ensure data integrity, reproducibility, and compliance with ethical guidelines.
• Contribute to grant writing and funding proposals.

• Ph.D. in Bioinformatics, Computational Biology, Statistics, Computer Science, or a related field.
• Minimum of 7 years of experience in data science and bioinformatics, with a focus on genomics.
• Extensive experience with statistical genetics methods and software (e.g., PLINK, GCTA).
• Proficiency in programming languages such as Python or R and their scientific libraries (e.g., NumPy, SciPy, Pandas, Scikit-learn).
• Experience with NGS data analysis pipelines (e.g., alignment, variant calling, annotation).
• Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, Azure) for large-scale data analysis.
• Strong understanding of molecular biology and genetics.
• Excellent communication, presentation, and interpersonal skills.
• Demonstrated leadership experience in a research setting.
• Ability to work effectively in a fully remote, collaborative team environment.

• Design and conduct cutting-edge research in data analytics and related fields.
• Develop and implement advanced statistical models and machine learning algorithms.
• Analyze large and complex datasets to identify trends, patterns, and actionable insights.
• Formulate hypotheses, design experiments, and validate research findings.
• Translate research outcomes into practical applications and recommendations for business strategy.
• Collaborate with cross-functional teams to define research objectives and integrate findings.
• Develop and maintain robust code for data analysis and modeling.
• Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence.
• Mentor junior researchers and contribute to the growth of the research team.
• Prepare and present research findings through publications, reports, and presentations.

• Ph.D. or equivalent in Statistics, Mathematics, Computer Science, Physics, or a related quantitative field.
• Minimum of 6 years of post-doctoral or relevant industry research experience.
• Demonstrated expertise in statistical modeling, machine learning, and data mining.
• Proficiency in programming languages such as Python (with libraries like NumPy, Pandas, Scikit-learn) or R.
• Experience with large-scale data processing and database technologies.
• Strong understanding of scientific research methodologies and experimental design.
• Exceptional analytical, problem-solving, and critical thinking skills.
• Excellent written and verbal communication skills for presenting complex technical information.
• Proven ability to work independently and collaborate effectively in a remote research environment.