Data Scientist

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.

Responsibilities:

1. Data Mining: Identify and extract valuable data from various sources such as databases, APIs, and web scraping.
2. 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.
3. Data Preprocessing: Clean and format both structured (like SQL databases) and unstructured data (like text or images) to prepare it for analysis.
4. Data Integrity: Implement processes to ensure data quality by validating and cleansing datasets to eliminate inconsistencies and errors.
5. Pattern Analysis: Employ statistical methods and machine learning techniques to analyze large datasets, uncover trends, and derive actionable insights.
6. Predictive Modeling: Design and develop algorithms that predict outcomes based on historical data, helping to inform strategic business decisions.
7. Solution Proposals: Suggest data-driven strategies to address specific business problems, leveraging analytical insights to improve decision-making.
8. Collaboration: Work closely with cross-functional teams, including Business and IT, to align data initiatives with organizational goals and ensure technical feasibility.

Requirements:

1. Educational Background: A degree in Computer Science, Engineering, or a related field.
2. Proven Experience: Demonstrated minimum 2 years experience in Data Scientist role, showcasing the ability to apply these skills in real-world scenarios.
3. Programming Skills: Proficiency in languages like R and Python for statistical analysis and machine learning, alongside SQL for querying databases.
4. Statistics: Strong foundation in applied statistics, understanding key concepts such as statistical tests, probability distributions, regression analysis, and maximum likelihood estimation.
5. Machine Learning: Familiarity with various machine learning algorithms, enabling the selection of appropriate models for specific tasks.
6. Data Wrangling: Ability to preprocess and clean datasets, addressing issues like missing values, inconsistencies, and outliers to ensure data quality.
7. 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.
8. Analytical Mind and Business Sense: Ability to think critically about data in a business context, linking insights to strategic objectives and operational decisions.
9. Experience with big data technologies (e.g., Hadoop, Spark) is a plus.
10. Familiarity with cloud platforms like AWS, Azure, or Google Cloud.

Consultant in-charge:

Tracy Lee | 010-391-2633 | tracy.lee@hunters-in.com