Data Scientist

SourcingXPress; Surat; On-site; INR 300,000 - 600,000; Full time

Job summary

VideoSDK is seeking a Data Scientist to leverage Python, statistics, and machine learning to extract insights and solve data-driven product problems. You will build models, preprocess data, and collaborate across teams to improve products.

The role emphasizes data-driven decision making, technical rigor, and delivering actionable analytics with dashboards and reports.

Qualifications

• Strong Python programming and ML fundamentals.
• Solid understanding of statistics and probability.
• Experience with data preprocessing, feature engineering and model evaluation.

Responsibilities

• Collect, clean, and analyze structured and unstructured data.
• Perform EDA to identify patterns and insights.
• Build, train, and evaluate ML/statistical models.
• Develop data pipelines and prepare datasets for analytics.
• Create data visualizations, dashboards, and reports to communicate insights.

Skills

Python

DSA

Statistics

Machine Learning

Pandas

NumPy

Scikit-learn

Matplotlib/Seaborn

SQL

Data preprocessing

Job description

Job Description

Company: VideoSDK

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Startup

Company Type: Product

Business Model: B2B

Funding Stage: Pre-seed

Industry: Information Technology

Salary Range: ₹ 3-6 Lacs PA

We are looking for a Data Scientist with strong expertise in Python, statistics, and machine learning to join our team. The ideal candidate will be passionate about working with data, building machine learning models, uncovering meaningful insights, and solving complex business and product problems through data-driven approaches.

Key Responsibilities

• Collect, clean, process, and analyze both structured and unstructured data.
• Perform Exploratory Data Analysis (EDA) to identify meaningful patterns, trends, and insights.
• Build, train, evaluate, and optimize machine learning and statistical models.
• Develop data pipelines and prepare high-quality datasets for analytics and machine learning use cases.
• Perform data preprocessing and feature engineering to improve model performance.
• Evaluate model accuracy, performance, and efficiency and continuously identify opportunities for improvement.
• Apply statistical and analytical techniques to support product and business decisions.
• Create meaningful data visualizations, dashboards, and reports to communicate insights effectively.
• Collaborate with engineering, product, and other cross-functional teams to solve data-driven problems.
• Work with large datasets and ensure data quality, consistency, and reliability.
• Write clean, maintainable, efficient, and well-documented code.
• Stay up to date with the latest developments in Machine Learning, Data Science, Artificial Intelligence, and related technologies.

Required Technical Skills

• Strong proficiency in Python.
• Good understanding of Data Structures and Algorithms (DSA).
• Strong understanding of Statistics and Probability.
• Solid knowledge of Machine Learning fundamentals.
• Hands-on experience with Pandas, NumPy, Scikit-learn, and Matplotlib/Seaborn.
• Understanding of data preprocessing, feature engineering, model evaluation, and validation.
• Basic knowledge of SQL and relational databases.
• Strong analytical, logical reasoning, and problem-solving skills.
• Ability to work with and analyze large datasets.
• Good debugging and troubleshooting skills.
• Strong attention to data quality and accuracy.

Good To Have

• Exposure to Deep Learning and frameworks such as PyTorch or TensorFlow.
• Familiarity with NLP, Computer Vision, or Generative AI.
• Understanding of time-series data and forecasting.
• Experience working with APIs or backend systems.
• Familiarity with cloud platforms such as AWS or GCP.
• Projects, internships, or academic work involving ML/AI or data science.

Good to Have

• Exposure to Deep Learning and frameworks such as PyTorch or TensorFlow.
• Familiarity with NLP, Computer Vision, or Generative AI.
• Understanding of time-series data and forecasting techniques.
• Experience working with APIs or backend systems.Familiarity with cloud platforms such as AWS or GCP.
• Experience through ML/AI projects, internships, research, or academic work.