Company Overview:

We are looking for a skilled Data Scientist to analyze large datasets and derive actionable insights that fuel business strategies. In this role, you’ll transform data into meaningful insights, collaborate across teams, and support data-driven decisions that enhance customer engagement and business performance.

The responsibilities and requirements for a role that involves Machine Learning/Artificial Intelligence, API Integration, Rule-Based Algorithm Development and Postgres Database management.

Responsibilities:

1. Machine Learning Model Development:

– Design, develop, and optimise machine learning and AI models to address business challenges.

– Implement supervised, unsupervised, and reinforcement learning models as required.

– Regularly evaluate model performance and apply optimizations or re-train as needed to ensure model accuracy and relevance.

2. API Integration:

– Integrate machine learning models and data pipelines with external APIs to automate workflows and ensure data interoperability.

– Design, implement, and document RESTful APIs that facilitate the use of machine learning models in production.

– Monitor and maintain API performance, ensuring security, efficiency, and data integrity.

3. Rule-Based Algorithm Development:

– Develop and implement rule-based algorithms to handle specific business logic or requirements that do not require machine learning.

– Collaborate with domain experts to define and refine rules, ensuring they remain aligned with evolving business needs.

– Optimize rule-based logic to be efficient and scalable, ensuring it can handle large data volumes and integrate seamlessly with ML models when necessary.

4. PostgreSQL Database Management:

– Design and manage database schemas in PostgreSQL to store and retrieve large datasets used in AI/ML models.

– Write efficient queries, stored procedures, and views to support data extraction and transformation.

– Optimize database performance and manage indexing, partitioning, and other techniques to improve query performance.

– Ensure database security, backup, and disaster recovery procedures are in place.

5. Data Preprocessing and Feature Engineering:

– Perform data preprocessing tasks, including cleaning, normalizing, and transforming raw data for use in machine learning models.

– Implement feature engineering techniques to improve the performance and accuracy of models.

6. Collaboration and Documentation:

– Work closely with cross-functional teams, including data scientists, data engineers, and product managers, to understand and deliver on business objectives.

– Document processes, algorithms, and data flows to ensure reproducibility and ease of maintenance.

– Train and mentor team members on best practices in machine learning and database management.

Requirements:

Technical Skills:

– Proven experience with Machine Learning and AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

– Proficiency in API Development and Integration, especially with RESTful APIs, Fast APIs.

– Strong programming skills in Python (or another relevant language), with experience in both rule-based and statistical/ML approaches.

– Solid understanding of rule-based algorithm design and implementation.

– Strong knowledge and hands-on experience with PostgreSQL or similar relational databases.

– Proficiency in SQL, including writing complex queries, optimization techniques, and managing large datasets.

Experience:

– 2+ years of experience in a machine learning, data engineering, or software engineering role, with proven experience in building, deploying, and maintaining ML models in production.

– 1+ years of experience in database management, particularly with Postgres, with a solid understanding of relational database principles and performance optimization techniques.

– Experience with cloud platforms (e.g., AWS, Azure, or GCP) for deploying APIs and ML models is a plus.

– Familiarity with data preprocessing, data wrangling, and feature engineering.

Problem-Solving Skills:

– Ability to understand complex business requirements and translate them into scalable and efficient machine learning or rule-based solutions.

– Strong analytical skills, with an aptitude for troubleshooting and problem-solving in technical environments.

Soft Skills:

– Strong communication skills, both written and verbal, with the ability to explain complex technical concepts to non-technical stakeholders.

– Team player who can collaborate effectively across different departments and roles.

– High attention to detail, especially when working with large datasets and critical business logic.

Preferred Qualifications:

– Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.

– Relevant certifications in Machine learning, Aritifical Intelligence, Data science, cloud computing, databases, or software engineering are a plus.

– Portfolio showcasing past projects or contributions to open-source projects.