Data Science

V2 Solutions; Hinoba-an; On-site; PHP 600,000 - 1,200,000; Full time

Job summary

Diverse Lynx is seeking a Data Scientist to join our dynamic team and advance a rewarding career in data-driven decision making.

You will explore large datasets, build predictive models, and collaborate with engineers and analysts to embed insights into business processes. The role emphasizes feature engineering, data preprocessing, model evaluation, and continuous learning in a fast-paced environment.

Qualifications

• Analyze large datasets to identify trends, patterns, and correlations.
• Apply statistical methods and ML algorithms to derive actionable insights.
• Design, develop, and implement predictive models and machine learning algorithms.
• Collaborate with data engineers, business analysts, and stakeholders to understand project objectives and requirements.
• Communicate findings and insights to non-technical stakeholders in a clear and understandable manner.

Responsibilities

• Explore and analyze large datasets for trends and correlations.
• Build and evaluate predictive models and ML algorithms.
• Engineer features to improve model performance.
• Preprocess data ensuring quality and consistency.
• Validate models with appropriate metrics and techniques.
• Collaborate with cross-functional teams to meet project goals.
• Deploy models into production and integrate with workflows.
• Stay current with industry trends and technologies.

Skills

Data Analysis

Machine Learning

Statistical Methods

Python/R

Data Visualization

Job description

Diverse Lynx is looking for Data Science to join our dynamic team and embark on a rewarding career journey

Data Exploration and Analysis:

• Explore and analyze large datasets to identify trends, patterns, and correlations
• Utilize statistical methods and machine learning algorithms to derive actionable insights

Model Development:

• Design, develop, and implement predictive models and machine learning algorithms
• Collaborate with cross-functional teams to integrate models into business processes

Feature Engineering:

• Engineer features from raw data to improve model performance and accuracy
• Work on optimizing feature selection and extraction techniques

Data Preprocessing:

• Cleanse and preprocess raw data to ensure data quality and consistency
• Handle missing data, outliers, and other data-related challenges

Model Evaluation and Validation:

• Evaluate model performance using appropriate metrics and validation techniques
• Fine-tune models to improve accuracy, precision, and recall

Collaboration:

• Collaborate with data engineers, business analysts, and stakeholders to understand project objectives and requirements
• Communicate findings and insights to non-technical stakeholders in a clear and understandable manner

Deployment and Integration:

• Collaborate with IT teams to deploy models into production environments
• Integrate models into existing systems and workflows

Continuous Learning:

• Stay current with industry trends, advancements in data science, and emerging technologies
• Implement new methodologies and technologies to enhance data science capabilities