Deeply involved in the entire data lifecycle, from initial collection and preprocessing to sophisticated analysis and the development of actionable insights.
A core function will be the design, implementation, and validation of diverse analytical models that extract meaningful patterns and predictive capabilities from complex datasets.
This position demands a robust understanding and practical experience in several key areas, including data modeling techniques for various data structures (structured, semi-structured, and unstructured), a broad spectrum of machine learning algorithms (supervised, unsupervised, and reinforcement learning), and the principles and applications of deep learning architectures (such as convolutional neural networks and recurrent neural networks).
Data integrity: Clean, preprocess, and organize raw data into usable formats. Ensure data integrity, consistency, and quality.
Data Analysis and Exploration: Perform exploratory data analysis (EDA) to uncover trends, patterns, and anomalies. Generate descriptive statistics, visualizations, and summary reports.
Model Development and Machine Learning: Develop predictive or classification models using machine learning and statistical techniques. Select appropriate algorithms (e.g., regression, decision trees, clustering, neural networks). Train, test, and validate models using best practices to ensure performance and generalizability.
Research-Oriented Collaboration: Thrive in a dynamic, research-focused group with several concurrent projects, contributing to innovative solutions and high-impact outcomes.
Stakeholder Communication: Communicate findings, insights, and recommendations to all stakeholders, ensuring alignment and understanding.
Agile Development: Drive the adoption of agile methodologies, ensuring iterative progress and delivering high-quality solutions.
Collaborate Across Teams: Work closely with product design and engineering teams to understand their needs and translate them into actionable data solutions.
Persyaratan Khusus
What skills and experience do you need?
Minimum of 4 years of experience in data science or a related analytical role.
Bachelor’s degree (or equivalent) in Statistics, Applied Mathematics, Computer Science, Engineering, Data Science, or a related discipline.
Proven experience in data modeling, data mining, mathematics, statistical analysis, and their application in business contexts.
Expertise in designing and implementing pattern recognition systems and predictive models.
Strong proficiency in ML/DL frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience with MLflow (for experiment tracking and model management)
Expertise in programming languages like Python, R, or Java.
Experience with SQL and NoSQL databases.
Good analytical and problem-solving abilities.
A data-driven mindset with excellent analytical and critical thinking abilities.
Comfortable and practical working in a fast-paced, research-oriented group with multiple simultaneous projects.
Ability to communicate complex data in a simple, actionable way
Excellent communication skills in English both verbal and written