Job Description

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: ₹10,000.00 - ₹12,500.00 per month

Work Location: In person