About the company: The company is a prominent multi-level marketing (MLM) company specializing in health supplements, personal care products, and lifestyle goods.
They aim to empower individuals to achieve financial independence while promoting a healthy lifestyle.
Responsibilties: Data Mining: Identify and extract valuable data from various sources such as databases, APIs, and web scraping.
Feature Selection and Classification: Utilize machine learning algorithms to choose the most relevant features from the dataset, develop classifiers, and refine them for better accuracy.
Data Preprocessing: Clean and format both structured (like SQL databases) and unstructured data (like text or images) to prepare it for analysis.
Data Integrity: Implement processes to ensure data quality by validating and cleansing data sets to eliminate inconsistencies and errors.
Pattern Analysis: Employ statistical methods and machine learning techniques to analyze large datasets, uncover trends, and derive actionable insights.
Predictive Modeling: Design and develop algorithms that predict outcomes based on historical data, helping to inform strategic business decisions.
Solution Proposals: Suggest data-driven strategies to address specific business problems, leveraging analytical insights to improve decision-making.
Collaboration: Work closely with cross-functional teams, including Business and IT, to align data initiatives with organizational goals and ensure technical feasibility.
Requirements: Educational Background: A degree in Computer Science, Engineering, or a related field.
Proven Experience: Demonstrated Min 2 years experience in Data Scientist role, showcasing the ability to apply these skills in real-world scenarios.
Programming Skills: Proficiency in languages like R and Python for statistical analysis and machine learning, alongside SQL for querying databases.
Statistics: Strong foundation in applied statistics, understanding key concepts such as statistical tests, probability distributions, regression analysis, and maximum likelihood estimation.
Machine Learning: Familiarity with various machine learning algorithms, and enabling the selection of appropriate models for specific tasks.
Data Wrangling: Ability to preprocess and clean datasets, addressing issues like missing values, inconsistencies, and outliers to ensure data quality.
Hands-on Experience with Data Science Tools: Practical experience using various data science tools and libraries, such as scikit-learn, TensorFlow, or similar platforms, to implement machine learning and analysis tasks.
Analytical Mind and Business Sense: Ability to think critically about data in a business context, linking insights to strategic objectives and operational decisions.
Experience with big data technologies (e.g., Hadoop, Spark) is a plus.
Familiarity with cloud platforms like AWS, Azure, or Google Cloud.
Consultant in-charge: Tracy Lee |010-391-2633 | ******