Remote Data Scientist

JOB PURPOSE: The Data Scientist is responsible for applying advanced analytical and machine learning techniques to solve complex business problems. This role involves designing, developing, and deploying predictive models, as well as leading data science projects across the organization.

QUALIFICATIONS:

1. A degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.
2. Experience in finance, supply chain management, engineering, or manufacturing operations is a plus.

EXPERIENCE:

1. At least five years of experience in data science, machine learning, or a related field.
2. Proven track record of applying data science techniques to business functions such as financial analytics, manufacturing, sales, and marketing.

SKILLS AND ABILITIES REQUIRED:

1. Technical Skills: Proficiency in database programming languages, including SQL and PL/SQL. Experience with non-relational databases and distributed computing tools (e.g., Hadoop, MapReduce). Strong programming skills in Python, Java, Scala, or similar languages. Experience with machine learning platforms such as Microsoft Azure ML, Google Cloud ML, or similar.
2. Machine Learning and Data Science: In-depth knowledge of statistical and data mining techniques. Experience with predictive modeling, classification, clustering, and other machine learning tasks. Familiarity with deep learning, neural networks, and associated algorithms.
3. Problem-Solving and Innovation: Ability to develop innovative solutions to complex problems using data science techniques. Strong analytical thinking with a focus on continuous improvement.
4. Communication and Collaboration: Excellent communication skills to articulate complex models and insights to non-technical stakeholders. Ability to work with cross-functional teams and integrate domain knowledge into analytics solutions.

ROLE RESPONSIBILITIES:

1. Project Management and Problem Analysis: Lead data science projects from concept to deployment. Collaborate with business units to identify data-driven opportunities.
2. Data Exploration and Preparation: Conduct exploratory data analysis to understand the underlying mechanics of business processes. Prepare data for modeling and ensure its quality and integrity.
3. Model Development and Deployment: Develop and implement machine learning models to solve classification, prediction, and other analytical tasks. Collaborate with IT and other stakeholders to deploy models and ensure their performance.
4. Continuous Learning and Improvement: Stay updated with the latest trends in data science and machine learning. Mentor and train other team members on data science principles.