Offer summary

Qualifications:

Degree in Computer Science, Engineering, or related field., Minimum 1 year proven experience as a Data Scientist., Proficiency in R, Python, and SQL for data analysis and machine learning., Strong foundation in statistics and familiarity with data science tools like scikit-learn or TensorFlow..

Key responsibilities:

Perform data mining from various sources including databases and web scraping.

Apply machine learning algorithms for feature selection, classification, and predictive modeling.

Clean and preprocess structured and unstructured data to ensure quality.

Collaborate with cross-functional teams to align data initiatives with business goals.

Job description

About the company:

The company is a prominent multilevel 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:

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 datadriven strategies to address specific business problems, leveraging analytical insights to improve decisionmaking.
Collaboration: Work closely with crossfunctional teams, including Business and IT, to align data initiatives with organizational goals and ensure technical feasibility.