Intern Data Scientist
In this role, you will analyze complex data sets, develop models, and provide actionable insights
to support our business objectives. You will work closely with cross-functional teams to deliver
data-driven solutions that enhance decision-making and drive innovation.
Compensation: Selected candidates will join as an Intern Data Scientist and they will undergo
training for 6 months with starting stipend of Rs.10,000 per month to Rs.14,000 per month
depending upon the interview. After the training they will be offered a package of Rs. 2.5 to 3
Lacs per annum.
Eligibility Criteria: Candidate should be Graduates in Engineering (Electronics and
Communication, Computer Sciences and Information Technology), B.E. B. Tech, MCA, M.Sc.
Male candidates with minimum of 70% marks throughout Graduation
Similarly, Female Candidates with minimum of 70% marks throughout Graduation
Responsibilities:
 Assist in data collection, cleaning, preprocessing, and exploratory data analysis (EDA).
 Develop and implement machine learning models for classification, regression, clustering, and
NLP tasks.
 Optimize model performance through feature engineering, hyper parameter tuning, and
evaluation metrics.
 Work with structured and unstructured data to generate insights and predictions.
 Develop dashboards and reports for data visualization using tools like Tableau and Power BI.
 Collaborate with cross-functional teams, including software engineers and data analysts, to
integrate ML solutions.
 Document findings, methodologies, and results in a clear and structured manner.
 Stay updated with the latest trends and advancements in data science, machine learning, and
AI.
Requirements:
 Good understanding of machine learning algorithms and statistical methods.
 Strong analytical mindset with a keen interest in AI and data science.
 Proficiency in programming languages and tools such as Python, MySQL, MongoDB, PySpark,
Flask, FastAPI, Git, OpenCV, Docker, and MLflow.
 Strong understanding of concepts like Regression, Classification, Clustering, and foundational
knowledge of Deep Learning (RNN, ANN, CNN, LSTM, Transformers). Exposure to TensorFlow,
PyTorch, Hugging Face , and OpenAI is beneficial.
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 Intermediate knowledge of ETL Pipelines, Data Processing, and Statistical Modeling.
 Intermediate knowledge of Docker, Kubernetes, and an understanding of CI/CD Pipelines is a
plus.
 Ability to work with data visualization tools like Matplotlib and Seaborn. Exposure to Tableau
or Power BI is an advantage.
 Exposure to cloud platforms like GCP, AWS, or Azure is beneficial.
Soft Skills:
 Strong analytical and problem-solving skills
 Attention to detail and ability to work with complex datasets
 Good communication skills to explain technical concepts effectively
 Ability to work both independently and collaboratively in a team environment
 Adaptability and willingness to learn new technologies