Data Scientist Trainee

Core Responsibilities

Data Preparation and Analysis

• Data Collection and Extraction: Assist in identifying, collecting, and extracting structured and unstructured data from various sources (e.g., databases, APIs, log files) using tools like SQL.
• Data Cleaning and Preprocessing: Clean, transform, and preprocess raw data to ensure quality, consistency, and suitability for analysis. This involves handling missing values, outliers, and data standardization.
• Exploratory Data Analysis (EDA): Conduct initial Exploratory Data Analysis (EDA) using statistical methods and programming languages like Python or R to uncover patterns, identify trends, and understand data distributions.

Model Development Support

• Algorithm Application: Apply fundamental Machine Learning algorithms (e.g., linear regression, classification, clustering) to solve defined business problems.
• Model Training and Testing: Assist in training, testing, and evaluating the performance of basic predictive models.
• Statistical Analysis: Perform statistical hypothesis testing and basic quantitative analysis to draw conclusions from data.

Communication and Collaboration

• Visualization and Reporting: Create clear and concise data visualizations (e.g., charts, graphs, dashboards using tools like Tableau or Matplotlib) to communicate findings to technical and non-technical stakeholders.
• Cross-functional Collaboration: Work closely with data analysts, data engineers, and business teams to understand project requirements and align data solutions with business objectives.
• Problem-Solving: Utilize a curious, analytical, and problem-solving mindset to propose and investigate data-driven solutions to business challenges.

Expected skillset

· Bachelor's or Master's degree in Computer science, Data Science or related field

· Familiarity with cloud platforms for scalable data processing and storage, such as Microsoft Azure or AWS

· Proficiency in Python libraries; Pandas (data manipulation), Numpy (numerical operations), Sci-kit learning (machine learning) and SQL for extracting data.

· Understanding and implementing common ML algorithms (e.g., linear/logistic regression, decision trees, clustering).

· Train machine learning and deep learning models using appropriate algorithms (linear, tree-based, boosting, neural nets, time-series & NLP methods).

· A strong grasp of statistical concepts (e.g., hypothesis testing, probability distributions, regression analysis) to interpret data and validate models.

· Validate via cross-validation/backtesting and perform robustness, bias, and fairness checks.

· Write tests for data pipelines, deploy and productionize models (Docker, APIs), and maintain model registry/versioning.

· Creating effective charts, graphs, and dashboards using tools like Matplotlib, Seaborn, Tableau, or Power BI to communicate insights.

Job Type: Fresher

Pay: ₹10, ₹12,500.00 per month

Work Location: In person