Data Scientist

AI Solution Development & Deployment:

• Design, develop, and deploy scalable AI/Machine Learning models and solutions to address complex business challenges across Product, Growth, Strategy, and Marketing teams.

• Conduct exploratory data analysis and compile diverse data sources (structured and unstructured) to prepare high-quality inputs for mathematical and AI models.

• Select, build, and validate optimal predictive and prescriptive models to ensure precision, effectiveness, and alignment with business objectives.

• Package AI/ML solutions as a robust and scalable automated computing pipeline.

• Partner closely with the Data Engineering team to integrate and deploy models into the production data infrastructure, ensuring reliability and performance.

• Monitor, measure, and continuously improve model accuracy and business impact over time.

Innovation & Collaboration:

• Collaborate with business stakeholders to understand complex challenges and define the most suitable analytical, machine learning, or statistical approaches.

• Actively translate new innovations and cutting-edge developments in AI/ML into practical, impactful applications for the organization.

• Constantly evaluate and propose new tools, technologies, and techniques to enhance data science capabilities within the company.

What we need from you (your skill and experience/ what you’ll bring to the team)

Core Technical & Educational Background:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics or a related quantitative field.

• At least 2 years of professional experience in a Data Scientist or related AI/ML role.

• Strong theoretical and practical background in machine learning and statistics, with proven hands-on experience in sourcing, cleaning, manipulating, and analyzing large volumes of data.

• Expert programming skills in Python (or R), demonstrated by previous projects, technical artifacts, and work experience.

Proven ability to apply expert knowledge of data frameworks and tools, including:

• Data Analytics Storage (e.g., Amazon Redshift / Amazon Athena / Amazon S3).

• Knowledge and experience in statistical and data mining techniques (e.g.,)

• GLM/Regression, Random Forest, Gradient Boosting, SVM, text embedding, etc.).

• Open-source machine learning libraries (e.g., scikit-learn, Weka), distributed/parallel big data processing architecture (e.g., Hadoop, Spark, MLlib), and deep learning frameworks (e.g., TensorFlow, Pytorch).

• Experience in the tech industry, fintech, and/or e-commerce is highly preferred.

Mindset & Skills for Success:

• A profound love for data, artificial intelligence, and innovation, evidenced by a curiosity to push boundaries and explore new frontiers.

• Self-Starter and Self-Learner: Demonstrated ability to proactively drive projects from concept to deployment and rapidly learn complex new topics, methodologies, and technologies with minimal guidance.

• High Resilience & Adaptability: Be willing to take action, challenge the status quo, and thrive in a fast-paced, dynamic environment.

• Comfortable with ambiguity and skilled at balancing multiple competing priorities effectively.

• Excellent communication skills with the ability to articulate complex concepts to both technical and non-technical stakeholders.