Data Scientist

Role Overview

We are seeking an experienced Data Scientist to join our Data & Analytics team and help transform complex datasets into meaningful business insights and intelligent solutions. The successful candidate will combine strong statistical thinking, programming expertise, and machine learning knowledge to solve real-world business problems.

Responsibilities

• Understand business requirements and translate them into data science problems.
• Collect, clean, transform, and analyze structured and unstructured datasets.
• Perform exploratory data analysis to identify trends, relationships, anomalies, and opportunities.
• Develop and evaluate machine learning and statistical models for prediction, classification, recommendation, and other use cases.
• Apply appropriate feature engineering and selection techniques to improve model performance.
• Work with supervised and unsupervised learning approaches based on project requirements.
• Explore NLP and deep learning techniques for relevant business applications.
• Train, validate, tune, and compare models using appropriate evaluation metrics.
• Build data visualizations and dashboards to communicate findings effectively to technical and non-technical stakeholders.
• Collaborate with data engineers to access, prepare, and optimize large-scale datasets.
• Package and deploy analytical or machine learning models through suitable application frameworks and APIs.
• Monitor model performance and contribute to model improvement and maintenance.
• Document methodologies, assumptions, experiments, results, and technical decisions.
• Stay informed about emerging developments in machine learning, AI, cloud technologies, and data science practices.

Requirements

• 4+ years of professional experience in Data Science, Machine Learning, or a closely related field.
• Strong programming experience with Python; working knowledge of R is desirable.
• Strong SQL skills with experience querying and analyzing relational data.
• Practical experience with machine learning algorithms and model development.
• Good understanding of statistics, probability, hypothesis testing, and model evaluation.
• Hands-on experience with libraries such as Pandas, NumPy, Scikit-learn, or equivalent.
• Experience with data visualization using Power BI, Tableau, Matplotlib, Seaborn, or similar tools.
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Experience handling data preparation, feature engineering, and model validation.
• Understanding of productionizing or deploying machine learning models.
• Strong analytical thinking and problem-solving ability.

Skills

• Python
• Scikit-learn
• TensorFlow
• SQL
• Pandas