Job Description

Job Summary:

We are looking for a skilled and motivated

Data Scientist

with 3 to 5 years of hands-on experience in data analysis, statistical modeling, and machine learning. The ideal candidate should be passionate about data-driven decision-making and able to translate business problems into actionable insights using advanced analytics and predictive modeling.

Key Responsibilities:

Work closely with stakeholders to understand business objectives and identify data-driven opportunities. Design, develop, and implement machine learning models and algorithms. Perform exploratory data analysis (EDA), feature engineering, and data preprocessing. Build predictive and classification models using tools like Python or R. Collaborate with data engineers to ensure data quality and pipeline efficiency. Visualize insights and present findings through dashboards and reports using tools like Power BI, Tableau, or similar. Communicate technical results to non-technical audiences clearly and effectively.

Required Skills:

Proficient in

Python

or

R

for data analysis and modeling. Strong understanding of

machine learning algorithms

,

statistics

, and

data mining

techniques. Hands-on experience with

SQL

and working with relational databases. Familiarity with libraries such as

Pandas, NumPy, Scikit-learn, TensorFlow, Keras

, etc. Experience with data visualization tools (e.g.,

Matplotlib

,

Seaborn

,

Power BI

,

Tableau

). Knowledge of cloud platforms such as

AWS

,

Azure

, or

GCP

is a plus. Solid problem-solving skills and attention to detail.

Preferred Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. Experience working with large datasets and unstructured data. Exposure to NLP or deep learning frameworks is a plus.
Job Type: Full-time

Pay: Up to ?1,800,000.00 per year

Schedule:

Day shift
Experience:

Total Work: 4 years (Preferred) Data Scientist: 3 years (Preferred) Python: 3 years (Preferred) Azure: 2 years (Preferred)
Work Location: In person