Junior Data Scientist

Strides Digitalstridesdigital.com.sg; Singapore; On-site; SGD 42,000 - 64,000; Full time

Job summary

Stride Digital seeks a Junior Data Scientist to design, develop, and deploy data-driven and AI-powered solutions addressing mobility and transport challenges.

You will build scalable analytics pipelines, develop ML models, and translate data into actionable insights for strategic decisions. Collaboration with data engineers, product managers, and business stakeholders is essential.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Analytics, Statistics or CS.
• 0–2 years of data science, analytics, or related experience.
• Proficient in Python or R and SQL.
• Experience with Pandas, NumPy, Scikit-learn, and visualization tools.

Responsibilities

• Design, develop, and deploy data-driven and AI-powered solutions.
• Build scalable analytics pipelines and ML models.
• Create dashboards and reports for stakeholders.
• Collaborate with data engineers and product teams.

Skills

Python

SQL

Statistics basics

Data analysis

Communication

Education

Bachelor’s or Master’s in Data Science/Analytics/Statistics/CS

Tools

Power BI

Tableau

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

Git

Job description

As a Junior Data Scientist, you will work closely with data engineers, product managers, and business stakeholders to design, develop, and deploy data-driven and AI-powered solutions that address real-world mobility and transport challenges. You will contribute to building scalable analytics pipelines, developing machine learning models, and translating data into actionable insights that support strategic decision-making.

Roles and Responsibilities:

• Perform data extraction, cleaning, transformation, and validation to ensure high-quality datasets for analysis and modeling.
• Conduct exploratory data analysis (EDA) using statistical techniques to uncover trends, patterns, correlations, and anomalies.
• Develop data visualizations, dashboards, and reports (e.g., Power BI, Python libraries) to communicate insights clearly to technical and non-technical stakeholders.
• Build, evaluate, and optimize machine learning models (e.g., regression, classification, clustering, time-series forecasting) to solve business problems.
• Support end-to-end model lifecycle including feature engineering, model training, validation, deployment, and performance monitoring.
• Collaborate with cross-functional teams to translate business problems into analytical use cases and deliver actionable insights.
• Assist in developing and maintaining data pipelines and workflows in collaboration with data engineering teams.
• Implement basic MLOps practices, including version control, reproducibility, model tracking, and documentation.
• Monitor model performance and data drift and propose improvements or retraining strategies where necessary.
• Ensure data governance, security, and privacy compliance in accordance with organizational and regulatory requirements.
• Document methodologies, datasets, assumptions, and results for transparency and reproducibility.
• Stay up to date with emerging tools, frameworks, and trends in data science, AI, and analytics to continuously improve team capabilities.

Qualifications/Experience:

• Bachelor’s or Postgraduate degree in Data Science, Data Analytics, Statistics, Operations Research, Computer Science, Artificial Intelligence, or related field.
• 0–2 years of experience in data science, analytics, or related roles (internships/projects included).
• Proficiency in at least one programming language: Python (preferred) or R, and strong working knowledge of SQL.
• Familiarity with data science libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
• Experience with data visualization tools (e.g., Power BI, Tableau, or equivalent).
• Exposure to cloud platforms (Azure, AWS, or GCP) is a plus.
• Understanding of software development practices (Git, code versioning, testing) is an advantage.

Skills Needed:

• Strong foundation in data analysis, statistics, and machine learning basics
• Proficiency in handling and analyzing large datasets with attention to detail
• Ability to derive insights and translate data into actionable outcomes
• Familiarity with Python/SQL and data visualization tools
• Eagerness to learn and adapt to new data tools, technologies, and analytical methods
• Good problem-solving and critical thinking skills
• Effective communication and teamwork abilities