Data Scientist

Job Description

Data Collection and Preparation

• Design Data Pipelines: Develop and implement robust data pipelines for efficient data collection, cleaning, and transformation from various sources, ensuring data quality and integrity.
• Preprocess Data: Handle complex data preparation tasks including data wrangling, feature engineering, and normalization to make data suitable for analysis and modeling.
• Integrate Diverse Data Sources: Combine and harmonize data from multiple sources, ensuring consistency and completeness in the datasets used for analysis.
  
  

Advanced Data Analysis and Modeling

• Conduct In-Depth Exploratory Data Analysis (EDA): Use advanced statistical techniques and visualization tools to uncover patterns, correlations, and anomalies in large and complex datasets.
• Perform Statistical Analysis: Apply statistical methods and hypothesis testing to derive actionable insights from data, supporting evidence-based decision-making.
• Develop Predictive Models: Design, build, and deploy sophisticated predictive models and machine learning algorithms tailored to address specific business problems and objectives.
  
  

Model Building and Evaluation

• Develop and Test Models: Build and refine machine learning models, including supervised and unsupervised learning techniques, ensuring they meet performance and reliability standards.
• Evaluate Model Performance: Use advanced metrics and techniques to assess model accuracy, precision, recall, and other relevant performance indicators, making improvements as necessary.
• Optimize Models: Continuously monitor and optimize models for better performance, incorporating feedback and new data to enhance their predictive capabilities.
  
  

Collaboration and Communication

• Collaborate with Cross-Functional Teams: Work closely with data engineers, business analysts, and other stakeholders to understand project requirements, develop solutions, and integrate data science insights into business processes.
• Present Findings: Clearly communicate complex data insights and technical results to non-technical stakeholders through reports, visualizations, and presentations, facilitating informed decision-making.
• Support Strategic Initiatives: Provide data-driven recommendations and strategic insights that contribute to the organization’s goals and objectives.
  
  

Project Management

• Lead Project Components: Take ownership of specific components of data science projects, managing tasks and deliverables to ensure successful project execution.
• Manage Timelines and Deliverables: Develop and manage project timelines, ensuring that tasks are completed on schedule and deliverables meet quality standards.
• Address Challenges: Identify and resolve challenges or issues that arise during the project lifecycle, ensuring minimal disruption and successful project outcomes.
  
  

Continuous Learning and Innovation

• Stay Updated with Trends: Continuously update your knowledge of the latest advancements in data science, machine learning, and technology to apply cutting-edge techniques and tools.
• Contribute to Innovation: Bring new ideas and innovative approaches to data analysis and modeling, contributing to the development of novel solutions and methodologies.
  
  

Quality Assurance

• Ensure Data Integrity: Apply rigorous quality assurance practices to ensure the accuracy, consistency, and reliability of data and models.
• Implement Best Practices: Follow industry best practices for data science and analytics, maintaining high standards of work and promoting continuous improvement.
  
  

Pre-requisite Skills & Experience

• Excellent problem-solving, analytical, and critical-thinking skills
• Strong communication and teamwork abilities, with the ability to convey complex technical concepts to non-technical stakeholders
• Proficiency in Python programming & machine learning libraries e.g., Scikit-learn, TensorFlow, Keras.
• Strong skills in SQL and experience working with large-scale relational databases
• Advanced data visualization skills using Microsoft Power BI
  
  

Our Commitment to a Culture of Inclusion & Belonging

Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.