61 Data Modeling jobs in Bahrain
Senior Data Scientist - Financial Modeling
Posted 12 days ago
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Junior Data Scientist - Predictive Modeling
Posted 22 days ago
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Job Description
Responsibilities:
- Assist in the collection, cleaning, and preprocessing of large datasets from various sources.
- Develop, test, and validate predictive models under the guidance of senior team members.
- Perform exploratory data analysis to identify trends, patterns, and insights.
- Contribute to the development of data visualizations to communicate findings effectively.
- Learn and apply various machine learning algorithms and techniques.
- Support the deployment and monitoring of predictive models.
- Collaborate with team members to understand project requirements and objectives.
- Document methodologies, code, and findings clearly and concisely.
- Participate in team meetings and contribute to discussions on data-driven strategies.
- Gain practical experience with industry-standard data science tools and platforms.
Qualifications:
- Currently pursuing a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
- Foundational understanding of statistical concepts and methods.
- Proficiency in at least one programming language commonly used in data science, such as Python or R.
- Familiarity with data manipulation libraries (e.g., Pandas, NumPy) and machine learning libraries (e.g., Scikit-learn).
- Basic knowledge of SQL for database querying.
- Strong analytical and problem-solving skills.
- Eagerness to learn and adapt to new technologies and methodologies.
- Excellent communication and interpersonal skills.
- Ability to work independently and manage time effectively in a remote environment.
- A genuine interest in artificial intelligence and its applications.
This remote internship is a fantastic launchpad for a career in data science. Our operations are centered around Tubli, Capital, BH , but this role is entirely remote.
Senior Insurance Data Scientist - Risk Modeling
Posted 8 days ago
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Job Description
Responsibilities:
- Develop, validate, and deploy advanced statistical and machine learning models for risk assessment and prediction in insurance.
- Analyze large, complex datasets to identify patterns, trends, and correlations relevant to insurance risk.
- Collaborate with actuarial and underwriting teams to refine pricing strategies and reserve calculations.
- Implement data mining techniques for fraud detection and prevention.
- Perform rigorous model testing and validation to ensure accuracy and reliability.
- Communicate complex technical findings and recommendations to business stakeholders effectively.
- Stay current with industry best practices and emerging technologies in data science and actuarial science.
- Contribute to the development and maintenance of data infrastructure and pipelines.
- Mentor junior data scientists and analysts.
- Document methodologies, models, and results thoroughly.
Qualifications:
- Ph.D. or Master's degree in Statistics, Mathematics, Computer Science, Actuarial Science, or a related quantitative field.
- Minimum of 5 years of experience in data science, with a significant focus on the insurance or financial services industry.
- Proven experience in building and deploying predictive models for risk, pricing, or fraud detection.
- Proficiency in programming languages such as Python or R, and relevant libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
- Experience with SQL and big data technologies (e.g., Spark, Hadoop).
- Strong understanding of statistical modeling, machine learning techniques, and experimental design.
- Knowledge of actuarial principles and insurance product lines is highly desirable.
- Excellent analytical, problem-solving, and critical thinking skills.
- Strong communication and presentation skills.
- Ability to work independently and manage multiple projects in a remote setting.
Remote Principal Data Scientist - Financial Modeling
Posted 10 days ago
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Remote Lead Data Scientist (Financial Modeling)
Posted 16 days ago
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Job Description
Responsibilities:
- Lead the design, development, and implementation of advanced statistical and machine learning models for financial applications.
- Analyze large, complex financial datasets to identify trends, patterns, and actionable insights.
- Develop predictive models for risk management, credit scoring, fraud detection, market forecasting, and algorithmic trading strategies.
- Collaborate with quantitative analysts, traders, and portfolio managers to understand business needs and translate them into data science solutions.
- Mentor and guide a team of data scientists, fostering a culture of innovation and technical excellence.
- Evaluate and select appropriate algorithms and tools for specific modeling tasks.
- Ensure the robustness, scalability, and accuracy of deployed models.
- Communicate complex analytical findings and model insights to both technical and non-technical audiences.
- Stay abreast of the latest research and advancements in data science, machine learning, and financial technology.
- Contribute to the development of data infrastructure and best practices within the data science team.
- Ph.D. or Master's degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
- Extensive experience (7+ years) as a Data Scientist, with a significant focus on financial modeling and quantitative finance.
- Proven leadership experience managing and mentoring data science teams.
- Deep expertise in machine learning techniques (e.g., regression, classification, time series analysis, deep learning) and statistical modeling.
- Proficiency in programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and SQL.
- Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
- Strong understanding of financial markets, instruments, and regulatory environments.
- Excellent problem-solving skills and the ability to work independently in a remote, fast-paced environment.
- Superb communication and presentation skills, with the ability to explain complex models to diverse stakeholders.
- Experience with financial data providers (e.g., Bloomberg, Refinitiv) is a plus.
Principal Insurance Data Scientist - Risk Modeling
Posted 17 days ago
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Job Description
Responsibilities:
- Design, develop, and implement advanced statistical and machine learning models for insurance risk assessment, pricing, and forecasting.
- Analyze large datasets to identify trends, patterns, and insights related to insurance policies, claims, and customer behavior.
- Develop predictive models for fraud detection and prevention.
- Collaborate with actuarial teams to refine pricing strategies and ensure regulatory compliance.
- Build and maintain data pipelines and analytical frameworks to support ongoing modeling efforts.
- Evaluate the performance of existing models and implement improvements as needed.
- Stay current with the latest advancements in data science, machine learning, and actuarial science.
- Communicate complex analytical findings and recommendations to stakeholders, including senior management and non-technical teams.
- Mentor junior data scientists and contribute to the development of the data science team.
- Ensure the ethical and responsible use of data in all analytical activities.
Qualifications:
- Ph.D. or Master's degree in Data Science, Statistics, Mathematics, Actuarial Science, Computer Science, or a related quantitative field.
- Minimum of 8 years of experience in data science, with a strong focus on the insurance or financial services industry.
- Proven expertise in developing and deploying predictive models using statistical and machine learning techniques (e.g., regression, classification, time series analysis, NLP, deep learning).
- Proficiency in programming languages such as Python or R, and experience with data manipulation libraries (e.g., Pandas, NumPy).
- Strong knowledge of SQL and experience with database management.
- Familiarity with actuarial principles and insurance product lifecycles.
- Excellent analytical, problem-solving, and critical thinking skills.
- Exceptional communication and presentation skills, with the ability to explain complex models to diverse audiences in a remote setting.
- Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
- Ability to work independently and manage multiple projects effectively in a remote environment.
Remote Senior Data Scientist - Financial Modeling
Posted 21 days ago
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Job Description
Responsibilities:
- Design, develop, and implement complex statistical and machine learning models for financial forecasting, risk assessment, and fraud detection.
- Analyze large, intricate datasets to extract actionable insights and identify trends relevant to financial markets and customer behavior.
- Collaborate with cross-functional teams, including finance, product, and engineering, to define modeling requirements and integrate solutions.
- Build and maintain robust data pipelines and ETL processes for model training and deployment.
- Evaluate model performance, conduct A/B testing, and iterate on models to improve accuracy and efficiency.
- Communicate complex findings and recommendations clearly and concisely to both technical and non-technical stakeholders.
- Mentor junior data scientists and contribute to the team's technical growth.
- Stay current with the latest advancements in data science, machine learning, and financial analytics.
- Ensure data integrity, model explainability, and adherence to ethical data practices.
- Contribute to the strategic roadmap for data science initiatives within the organization.
Qualifications:
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
- Extensive professional experience as a Data Scientist, with a strong focus on financial modeling and analysis.
- Proficiency in programming languages such as Python or R, and experience with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, pandas, NumPy).
- Deep understanding of statistical modeling, machine learning algorithms, and time-series analysis.
- Experience with SQL and working with large-scale relational and non-relational databases.
- Familiarity with cloud platforms (AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop).
- Excellent problem-solving skills and the ability to translate business problems into data science solutions.
- Strong communication and presentation skills, with the ability to articulate technical concepts to diverse audiences.
- Proven ability to work independently and collaboratively in a remote team environment.
- Experience in the fintech or banking industry is a significant advantage.
This is an exceptional opportunity to make a significant impact in a remote capacity for a leader in financial technology. If you possess a passion for data and a track record of delivering impactful financial insights, we encourage you to apply.
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Senior Insurance Data Scientist, Risk Modeling
Posted 22 days ago
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Job Description
Responsibilities:
- Develop, implement, and validate advanced statistical and machine learning models for risk assessment and pricing.
- Conduct exploratory data analysis to identify trends, patterns, and insights within large insurance datasets.
- Design and engineer relevant features for predictive modeling.
- Evaluate and compare different modeling techniques to select the most appropriate solutions.
- Deploy models into production environments and monitor their performance.
- Collaborate with actuarial, underwriting, and product teams to understand business needs and translate them into analytical solutions.
- Communicate complex analytical findings and recommendations to stakeholders through clear reports and presentations.
- Stay current with the latest advancements in data science, machine learning, and insurance analytics.
- Mentor junior data scientists and contribute to team knowledge sharing.
- Master's or Ph.D. in Statistics, Data Science, Computer Science, Economics, or a related quantitative field.
- 5+ years of experience as a Data Scientist, with a significant focus on risk modeling or actuarial science.
- Proficiency in programming languages like Python or R, and relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with SQL and working with large, complex datasets.
- Strong understanding of statistical modeling, machine learning algorithms, and experimental design.
- Familiarity with insurance industry data and concepts is highly preferred.
- Experience with cloud platforms (AWS, Azure, GCP) and big data technologies is a plus.
- Excellent analytical, problem-solving, and communication skills for a remote setting.
Lead Data Scientist - Financial Risk Modeling
Posted 14 days ago
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Senior Data Scientist - Insurance Risk Modeling
Posted 17 days ago
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Job Description
Responsibilities:
- Design, develop, and implement advanced statistical and machine learning models for insurance risk assessment, including predictive modeling, fraud detection, and customer lifetime value analysis.
- Analyze large and complex datasets to identify trends, patterns, and insights relevant to insurance operations and risk management.
- Collaborate with actuaries, underwriters, and product development teams to translate business needs into data-driven solutions.
- Build and maintain robust data pipelines and analytical frameworks to support ongoing modeling efforts.
- Evaluate the performance of existing models, identifying areas for improvement and implementing enhancements.
- Stay abreast of the latest advancements in data science, machine learning, and actuarial science, and champion their adoption where appropriate.
- Communicate complex findings and model methodologies clearly and concisely to both technical and non-technical stakeholders.
- Ensure all modeling activities comply with regulatory requirements and ethical best practices.
- Mentor junior data scientists and contribute to the growth of the data science community within the organization.
- Master's or PhD in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
- A minimum of 7 years of experience in data science, with a significant focus on risk modeling within the insurance or financial services sector.
- Expertise in programming languages such as Python or R, and proficiency with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).
- Strong experience with machine learning algorithms (e.g., regression, classification, clustering, deep learning) and relevant libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
- Familiarity with SQL and experience working with large relational databases.
- Knowledge of actuarial principles and insurance industry data is highly desirable.
- Excellent analytical, problem-solving, and critical thinking skills.
- Strong communication and presentation skills, with the ability to convey complex technical concepts effectively.
- Proven ability to work independently and collaboratively in a fully remote, fast-paced environment.