Data Scientist (Onsite)

Qualifications

Data Science & Analytics

• Data Science Foundations: Strong foundation in data science, statistics, and advanced data analytics, including data visualization to communicate insights effectively.
• Exploratory Data Analysis (EDA): Skilled in performing EDA to uncover patterns, detect anomalies, and generate meaningful insights from data.
• Experimentation & Testing: Skilled in designing A/B tests or other experimental designs to measure business impact, analyze results, and communicate findings clearly to stakeholders.

Machine Learning & AI

• Model Development & Deployment: Experience in building, deploying, and optimizing machine learning models on large datasets.
• Generative AI (GenAI): Opportunity to work on GenAI projects that drive innovation and impactful business solutions.

Problem-Solving & Collaboration

• Analytical & Problem-Solving Skills: Strong analytical and problem-solving abilities focused on deriving actionable insights from data.
• Team Collaboration: Ability to work effectively both independently and as part of a collaborative team, contributing to shared project goals.

Technical Expertise

• Proficiency in Big Data Technologies: Expertise in Spark, PySpark, and SQL for large-scale data processing focused on feature creation for machine learning models and data analysis tasks.
• Programming Skills: Strong proficiency in Python for data analysis and machine learning (including libraries like Pandas, PySpark, Scikit-learn, XGBoost, LightGBM, Matplotlib, Plotly, Seaborn, etc.).
• Python Notebooks: Familiarity with Jupyter, Google Colab, or Apache Zeppelin for interactive data analysis and model development.
• Platform Experience: Experience in using PySpark on cloud platforms such as Azure Databricks or other platforms (including on-premises) is a plus.

Education & Experience

• Educational Background: Bachelor’s or advanced degree in Data Science, Statistics, Computer Science, Computer Engineering, Mathematics, Information Technology, Engineering, or related fields.
• Work Experience: At least 2-3 years of relevant experience in Data Science, Analytics, or Machine Learning, with demonstrated technical expertise and a proven track record of driving data-driven business solutions.