We are looking for a Data Scientist to join our team and work on data-driven problem solving across analytics, machine learning, and predictive modeling. This role involves working with structured and unstructured data, building models, generating actionable insights, and supporting the development of intelligent, scalable solutions for business and engineering applications.
The ideal candidate should be comfortable working across the full data science lifecycle, from data cleaning and exploration to feature engineering, model building, validation, and deployment support.
Key Responsibilities
Analyze large and complex datasets to identify trends, patterns, and actionable insights.
Develop and deploy machine learning and statistical models for prediction, classification, forecasting, and anomaly detection.
Build and maintain data preprocessing and feature engineering pipelines.
Work with structured, semi-structured, and time-series data from multiple sources.
Collaborate with cross-functional teams to understand business problems and translate them into data science solutions.
Design experiments, evaluate model performance, and improve model accuracy and reliability.
Create dashboards, reports, and visualizations to communicate findings to technical and non-technical stakeholders.
Support development of scalable analytics workflows and data products.
Work with cloud platforms and databases for data storage, processing, and model workflows.
Document methodologies, assumptions, results, and recommendations clearly.
Required Qualifications
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
1-3 years of experience in data science, machine learning, advanced analytics, or applied statistics.
Strong programming skills in Python.
Good hands-on experience with:
Pandas, NumPy, SciPy
scikit-learn
Data visualization tools such as Matplotlib, Plotly, or similar
Strong understanding of:
Regression, classification, clustering
Feature engineering
Model evaluation and validation
Statistical analysis
Time-series analysis
Experience working with SQL and relational or analytical databases.
Experience with at least one cloud platform: AWS, GCP, or Azure.
Familiarity with cloud-based data and analytics services such as storage, compute, ETL, or managed ML tools.
Experience handling real-world noisy datasets, missing values, and data quality issues.
Strong problem-solving and analytical skills.
Preferred Qualifications
Experience building end-to-end machine learning pipelines.
Familiarity with big data tools or distributed data processing frameworks.
Exposure to model deployment, MLOps, or production analytics workflows.
Experience with experimentation, A/B testing, or causal analysis.
Familiarity with data warehousing and ETL pipelines.
Knowledge of version control tools such as Git.
Experience with dashboarding or BI tools is a plus.
