16 Data Architect jobs in Bahrain
Senior Manager, Data Architect
Posted today
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Job Description
- Drives the strategic vision and technical implementation of data architecture.
- Strong data architecture principles with a solid foundation in data modelling.
About Our Client
Leading investment financial services firm, renowned for its innovative investment strategies, strong market presence, and commitment to technological excellence.
Job Description
- Data Architecture Strategy: Define and evolve the firm's enterprise data architecture strategy, including data governance, data lifecycle management, data security, and data quality frameworks, aligned with business objectives and regulatory requirements.
- Architectural Design & Oversight: Lead the design and implementation of scalable, robust, and high-performance data architectures for transactional systems, data warehouses, data lakes, and analytical platforms, ensuring architectural integrity across all data initiatives.
- Data Modelling Expertise: Provide expert guidance and hands-on contributions to logical and physical data modelling for complex financial datasets, ensuring optimal database design, data integrity, and performance for various applications.
- Technology Evaluation & Selection: Evaluate and recommend appropriate data technologies, tools, and platforms (e.g., relational databases, NoSQL, data warehousing solutions, cloud data services, streaming platforms) that best fit the firm's architectural vision and business needs.
- Vendor & Consulting Firm Management: Collaborate effectively with external technology vendors and consulting firms, overseeing their contributions to data architecture projects and ensuring alignment with the firm's strategic data roadmap.
The Successful Applicant
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related quantitative field.
- Minimum of 10+ years of progressive experience in data architecture, data engineering, or a related field, with at least 4 years in a senior architect or leadership role within the financial services or investment sector.
- Exceptional and demonstrated experience in data modelling (conceptual, logical, and physical), schema design, and database normalisation/denormalisation techniques, with a strong understanding of their impact on performance and scalability.
- Extensive experience across the breadth of data architecture domains including data warehousing, data lakes, ETL/ELT processes, data governance, metadata management, and data security.
What's On Offer
An exceptional opportunity for the successful candidate to lead the data architecture transformation at a prominent investment financial services firm in Bahrain.
Contact
Manpreet Kaur
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Senior Remote Insurance Data Architect
Posted 1 day ago
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Job Description
Responsibilities:
- Design and implement scalable data architectures for the insurance sector.
- Develop and maintain enterprise data models and schemas.
- Define and enforce data governance policies and standards.
- Select and evaluate appropriate data technologies and platforms.
- Ensure data quality, integrity, and security across systems.
- Collaborate with data engineers and analysts on data solutions.
- Translate business requirements into technical data designs.
- Optimize data pipelines and database performance.
- Provide technical leadership in data architecture initiatives.
- Stay abreast of industry trends and best practices in data management.
Qualifications:
- Extensive experience in data architecture, particularly within the insurance industry.
- Strong knowledge of insurance business processes and data needs.
- Expertise in data warehousing, big data technologies, and cloud platforms.
- Proficiency in data modeling tools and techniques.
- Experience with SQL, NoSQL databases, and ETL processes.
- Excellent analytical, problem-solving, and communication skills.
- Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.
Senior Data Scientist - Financial Modeling
Posted 12 days ago
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Job Description
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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Job Description
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.
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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.
Senior Insurance Data Scientist, Risk Modeling
Posted 22 days ago
Job Viewed
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.