Freelance Machine Learning Data Annotation Jobs in Minnesota

• Independently identify, analyze, and interpret patterns in our data using the appropriate technique for the situation, ranging from simple data aggregation to state-of-the-art machine learning (ML) methods.


• Understand our business processes and data so we can develop custom models that inform business decisions.


• Ensure our models are explainable to build confidence in our results and stakeholder trust, leveraging both statistical methods and advancements in explainable ML/AI.


• Lead in the development of processes and tools to monitor and analyze model performance and data accuracy.


• Collaborate with stakeholders and data partners to define data requirements needed to support the business processes, translate insights, and facilitate decision making.


• Act as the source of unbiased data-supported insights to the organization; confidently challenge norms when novel insights are discovered.


• Leads the mentorship and supports data scientists.


• Leads project subject matter experts in the evaluation of current business processes and identification of current weaknesses or inefficiencies.

• Knowledge of:


• Proficiency in Python and SQL.


• Experience operationalizing statistical and machine learning models in a production environment.


• Experience writing SQL queries with large datasets and complex, integrated systems.


• Experience in designing and implementing data applications, including deploying them to production environments.


• Expertise in data science, machine learning, data mining, operations research, and statistical modeling techniques, specifically for high-volume and complex datasets.


• Knowledge of best coding practices.


• Ability to:


• Choose the right tool from the data science toolkit for the task at hand, ranging from exploratory data analysis (EDA) to automated data pipelines in production.


• Prioritize work effort across multiple projects and articulate when a project is reaching the point of diminishing returns.


• Get up to speed and continue development on existing data science projects.


• Keep up with advancements in ML/AI and incorporate them to improve our processes and ability to generate insights.


• Collaborate with other data teams, including Business Intelligence, Data Governance, and Data Engineering.


• Display data visually, creating powerful presentations and stories which effectively quantify and justify value created to both analytical and non-analytical audiences.


• Lead and speak courageously, challenge ideas and propose alternatives.


• Grasp the diversity of our systems and network and the ability to make recommendations and implement solutions.


• Strong attention to detail and excellent organizational skills.


• Well-developed interpersonal skills dealing with people of different cultures and styles.


• Advanced communication skills to include writing and oral abilities to connect with a wide range of people.


• Demonstrate excellent analytical, problem solving with a strong customer focus.


• Manage multiple assignments simultaneously with minimal direction or supervision.


• Work and deliver under tight timeframes.

Proficient in Python and SQL, with expertise in machine learning, data science, and statistical modeling techniques. Capable of developing and operationalizing data applications while ensuring model explainability and performance monitoring.

• Python Proficiency


• SQL Query Writing


• Machine Learning Model Operationalization


• Data Visualization Skills


• Statistical Modeling Expertise

• Data Science


• Machine Learning


• Data Mining


• Statistical Modeling


• Data Application Design


• Exploratory Data Analysis


• Automated Data Pipelines


• Data Accuracy Monitoring


• Complex Dataset Management


• Best Coding Practices

• Attention to Detail


• Organizational Skills


• Interpersonal Skills


• Advanced Communication Skills


• Analytical Problem Solving

• Data Governance


• Business Intelligence


• Data Engineering


• Stakeholder Collaboration


• Model Explainability