8 Algorithm Development jobs in Bahrain

• Define the personalization and recommendation strategy aligned with business goals and client experience vision.
• Lead, mentor, and grow a team of data scientists and machine learning engineers.
• Collaborate cross-functionally with product, marketing, engineering, and commercial teams to embed data-driven personalization across all client touchpoints.
  
  

• Design, build, and deploy ML/AI models for:
• Personalized investment recommendations
• Smart content curation
• Behavioral and predictive analytics
• Funnel optimization and conversion predictions
• Drive experimentation and continuous model performance improvement using A/B testing and data validation techniques.
• Ensure model explainability, fairness, and compliance with relevant data privacy regulations.
  

• Architect and oversee the personalization engine powering mobile, web, and CRM systems.
• Implement real-time data processing pipelines for adaptive personalization.
• Collaborate with data engineering to ensure scalable, high-quality data infrastructure and feature pipelines.
  
  

• Translate data into actionable insights to inform client segmentation, lifecycle management, and journey personalization.
• Develop dashboards and KPIs to measure impact of personalization initiatives on user engagement and business outcomes.
  
  

• Strong product sense with a deep understanding of client behavior in digital financial services.
• A passion for using data to improve lives and decision-making for HNW individuals.
• Strategic and hands-on: able to drive vision and execute technical implementation.
• Collaborative leadership and stakeholder communication skills.
• Highly analytical with strong attention to detail and data integrity.
  
  

• Master’s or PhD in Data Science, Machine Learning, Computer Science, or related field.
• 6+ years of experience in applied data science, with 2+ years in a leadership or managerial role.
• Proven experience developing personalization and recommendation systems at scale.
• Proficiency in Python, SQL, and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with data platforms such as Databricks, AWS/GCP/Azure, and real-time data systems (e.g., Kafka).
• Background in fintech, wealth management, or e-commerce personalization is a strong plus
  
  

The Family Office is an independent wealth management firm offering customized investment solutions in alternative asset classes, including private equity, private debt, and real estate. Serving high-net-worth individuals and families, we provide tailored strategies to address unique financial needs with a focus on transparency, diversification, and long-term value. With a commitment to excellence and decades of expertise, The Family Office helps clients preserve and grow their wealth across generations.

We are seeking a Data Science Manager & Head of Recommendations and Personalization to lead the development of intelligent, data-driven client experiences across our digital platforms. You will spearhead the design and implementation of machine learning models for personalized investment recommendations, content targeting, and client engagement optimization.

This is a high-impact role for a strategic data leader who thrives in a fast-paced environment and has experience building and scaling personalization systems—ideally in fintech, digital wealth, or consumer platforms.

• Define the personalization and recommendation strategy aligned with business goals and client experience vision.
• Lead, mentor, and grow a team of data scientists and machine learning engineers.
• Collaborate cross-functionally with product, marketing, engineering, and commercial teams to embed data-driven personalization across all client touchpoints.

• Design, build, and deploy ML/AI models for:
  • Personalized investment recommendations
  • Smart content curation
  • Behavioral and predictive analytics
  • Funnel optimization and conversion predictions
• Drive experimentation and continuous model performance improvement using A/B testing and data validation techniques.
• Ensure model explainability, fairness, and compliance with relevant data privacy regulations.

• Architect and oversee the personalization engine powering mobile, web, and CRM systems.
• Implement real-time data processing pipelines for adaptive personalization.
• Collaborate with data engineering to ensure scalable, high-quality data infrastructure and feature pipelines.

• Translate data into actionable insights to inform client segmentation, lifecycle management, and journey personalization.
• Develop dashboards and KPIs to measure impact of personalization initiatives on user engagement and business outcomes.

• Strong product sense with a deep understanding of client behavior in digital financial services.
• A passion for using data to improve lives and decision-making for HNW individuals.
• Strategic and hands-on: able to drive vision and execute technical implementation.
• Collaborative leadership and stakeholder communication skills.
• Highly analytical with strong attention to detail and data integrity.

• Master’s or PhD in Data Science, Machine Learning, Computer Science, or related field.
• 6+ years of experience in applied data science, with 2+ years in a leadership or managerial role.
• Proven experience developing personalization and recommendation systems at scale.
• Proficiency in Python, SQL, and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with data platforms such as Databricks, AWS/GCP/Azure, and real-time data systems (e.g., Kafka).
• Background in fintech, wealth management, or e-commerce personalization is a strong plus

• Assist in data cleaning, preprocessing, and feature engineering for various datasets.
• Develop and implement statistical models and machine learning algorithms under the guidance of senior team members.
• Perform exploratory data analysis to identify trends, patterns, and anomalies.
• Visualize data to communicate findings effectively to technical and non-technical stakeholders.
• Contribute to the development of data pipelines and automated reporting systems.
• Learn and apply various data mining techniques.
• Collaborate with business analysts to understand project requirements and translate them into data science solutions.
• Participate in team meetings and present findings and progress.
• Stay updated with the latest advancements and tools in data science and machine learning.
• Assist in A/B testing and experimental design.
• Write clear and concise documentation for code and processes.
• Develop a strong understanding of the business context and its data needs.

• Recent graduate with a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Strong foundation in statistics, probability, and mathematics.
• Proficiency in programming languages such as Python or R.
• Familiarity with data manipulation libraries (e.g., Pandas, NumPy).
• Exposure to machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Basic understanding of SQL and database concepts.
• Excellent analytical and problem-solving skills.
• Strong communication and interpersonal skills.
• Eagerness to learn and a proactive attitude.
• Ability to work effectively in a team environment.
• Prior internship or project experience in data analysis is a plus.

• Assisting in the collection, cleaning, and preprocessing of large datasets.
• Performing statistical analysis and applying machine learning algorithms under supervision.
• Developing data visualizations to communicate insights and findings.
• Contributing to the development and testing of predictive models.
• Collaborating with cross-functional teams to understand data needs and deliver solutions.
• Learning and applying new data science tools and methodologies.
• Documenting processes, methodologies, and results.
• Participating in team meetings and presenting findings.
• Developing a strong understanding of business problems and how data can solve them.
• Proactively identifying opportunities to improve data processes and analyses.

• Assisting in data cleaning, preprocessing, and feature engineering.
• Developing and implementing statistical models and machine learning algorithms.
• Conducting exploratory data analysis to identify trends and patterns.
• Creating compelling data visualizations to communicate findings.
• Participating in team meetings and contributing to project discussions.
• Learning and applying advanced data analysis techniques and tools.
• Collaborating with mentors to define project objectives and deliverables.
• Developing a portfolio of data science projects.

• Assisting in the collection, cleaning, and pre-processing of large datasets.
• Performing exploratory data analysis to identify trends and patterns.
• Developing and implementing machine learning models under supervision.
• Visualizing data and presenting findings to team members.
• Learning and applying various data science tools and programming languages (e.g., Python, R, SQL).
• Collaborating with cross-functional teams to understand data requirements.
• Contributing to the development of data-driven reports and dashboards.
• Participating in team meetings and knowledge-sharing sessions.
• Researching new data science techniques and methodologies.
• Assisting in the documentation of data processes and model development.

• Overseeing the design and execution of the graduate data science program, ensuring alignment with company objectives and industry best practices.
• Providing mentorship and technical guidance to graduate data scientists on their projects and career development.
• Developing and delivering training modules on advanced data science techniques, machine learning algorithms, and statistical modeling.
• Facilitating project-based learning experiences, helping graduates apply their skills to real-world business challenges.
• Evaluating the performance and progress of graduate participants, providing constructive feedback.
• Collaborating with hiring managers and HR to identify potential hires from the graduate pool and facilitate their integration into full-time roles.
• Keeping the program curriculum updated with the latest advancements in data science, AI, and machine learning.
• Organizing knowledge-sharing sessions, workshops, and networking events for the graduate cohort.
• Contributing to the company's data science initiatives by advising on research directions and technical challenges.
• Ensuring a positive and productive learning environment for all participants.

• Advanced degree (Master's or Ph.D.) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Minimum of 5 years of professional experience in data science, machine learning, or AI, with a strong track record of success.
• Proven experience in mentoring, coaching, or managing junior data scientists or interns.
• Deep expertise in statistical analysis, predictive modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and data visualization.
• Proficiency in programming languages such as Python or R, and experience with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas).
• Familiarity with big data technologies and platforms (e.g., Spark, Hadoop).
• Excellent communication, presentation, and interpersonal skills, with the ability to explain complex technical concepts to a diverse audience.
• Strong project management and organizational skills.
• A passion for education, talent development, and fostering innovation.
• Understanding of the graduate recruitment and development lifecycle is a plus.