Job Description
Job Summary
You will be responsible for end-to-end data science cycles, encompassing designing, training, implementing, evaluating, and monitoring machine learning models, and will also design and implement highly scalable tools and algorithms based on state-of-the-art Machine Learning and Deep Learning methodologies.
You will work across the ML stack, from researching models, working with large datasets, training, and tuning existing models to creating new models, deploying them at scale, analyzing results, and presenting findings to stakeholders across tech and business domains.
Job Objectives
Develop and implement advanced predictive models to optimize customer experiences and other business outcomes.
Analyze large and complex datasets to extract actionable insights and drive business decisions.
Interpret results and provide actionable insights to guide real-time decision-making within the business context.
Collaborate with cross-functional teams to ensure proper deployment and integration of ML models for new releases.
Job Responsibilities
Predictive Modeling and Deployment
Develop and implement advanced predictive models to forecast key business metrics such as sales, customer churn, or product demand.
Utilize predictive modeling to optimize customer experiences and other business outcomes.
Execute machine learning models, algorithms, and statistical techniques to analyze historical data and ensure scalability and efficiency.
Data Preparation and Analysis
Develop and use advanced software programs, algorithms, and query techniques to cleanse, integrate, and evaluate datasets for model inputs.
Analyze large and complex datasets to extract actionable insights and identify trends and patterns that can drive business decisions.
Identify manual human processes, understand user behaviors, and analyze use cases that can be augmented or automated.
Model Deployment and Interpretation
Deploy models into production environments and monitor their performance over time.
Apply statistical, mathematical, and predictive modeling techniques to build, maintain, and improve real-time decision systems.
Interpret results, develop insights within the business context, and provide guidance on risks and limitations.
Development & Documentation
Write the code as per agreed software design rules to keep it aligned with the rest of the code base.
Code the final implementation that the generated code is referring to.
Follow company software data protection and security guidelines in developing software.
Accurately estimate the time needed to complete an assigned task.
Identify possible causes of issues or problems.
Think through and recommend solutions when raising issues around code, requirements, etc.
Write technical design documentation that fully defines all application code.
Maintain detailed knowledge of iHorizons products and services.
Understand the business impact for labs outcomes.
Collaboration & Team Guidance
Stay updated on the latest research, learn new applications, tools, and technologies in the fields of data science and machine learning through intensive and focused effort.
Collaborate with technical and non-technical business partners to develop analytical dashboards describing ML algorithm findings to stakeholders.
Collaborate with other teams to perform code reviews and oversee proper deployment for new releases.
Actively mentor and support mid-level and junior developers in their professional growth.
Provide guidance on best practices in machine learning, code reviews, and project design.
Foster an inclusive and collaborative environment that encourages continuous learning and development within the team.
Oversee interactions with vendors and third-party service providers, including collaborating on the design and implementation of technical architectures, and acting as a point of contact for resolving technical issues. Maintain clear communication with internal stakeholders regarding vendor-related activities, updates, and issues to facilitate smooth collaboration and decision-making processes.
Job Requirements
Educational Qualification
Bachelor’s degree in data science, statistics, and computer science is a MUST.
Google Cloud - Professional Machine Learning Engineer Certificate
Google AgentSpace implementation experience
Previous Work Experience
3-4+ Years of experience in data science or machine learning.
Must have strong experience in at least one of the following areas:
Vision models
NLP models (Experience in Arabic NLP is a huge plus)
Skills and Abilities
Proficient in python, TensorFlow, keras and pytorch.
Good experience in:
SQL and non-relational databases.
Data analytics reports generation.
ML model development deployment.
