You are a strategic thinker passionate about driving solutions in Data Annotation and Validation. You have found the right team.
As a Data Analyst within our Asset Management Data Science team, you will be responsible for setting and improving our organizational objectives, and ensuring their consistent accomplishment.
Job Responsibilities
- Work on data labeling tools and annotate data for machine learning models. Sift through structured and unstructured data; identify the right content and annotate with the right label.
- Develop comprehensive test plans and strategies for data science projects, including data validation, model testing, and performance evaluation.
- Collaborate with stakeholders, including data scientists, data engineers, and product managers.
- Conduct thorough data validation and verification processes to ensure data accuracy and consistency.
- Design and execute test cases for models, ensuring they meet performance and accuracy standards.
- Validate model outputs and conduct regression testing to ensure consistent results.
- Utilize tools like Snorkel, Datasaur, and Apptek for model performance monitoring, data labeling, and speech annotation.
- Develop and maintain automated testing scripts and tools to streamline the QA process.
- Implement continuous integration and continuous deployment (CI/CD) practices for data science projects.
- Transcribe verbatim audio recordings, single and multi-speaker of varying dialects and accents, and identify relevant keywords and sentiment labels.
- Build a thorough understanding of data annotation and labeling conventions and develop documentation/guidelines for stakeholders and business partners
Required qualifications, capabilities, and skills
- At least 5 years of hands-on experience in data collection, analysis, or research.
- Proven experience in data quality assurance, data management, or a similar role.
- Experience in Python programming.
- Proficiency in data querying and validation using SQL, with experience in Snowflake .
- Experience in constructing dashboards to effectively visualize and communicate data insights.
- Experience with data annotation, labeling, entity disambiguation, and data enrichment.
- Familiarity with industry-standard annotation and labeling methods and tools like Label Studio, Snorkel, Datasaur, and Apptek.
- Familiarity with Machine learning and AI paradigms such as text classification, entity recognition, information retrieval.
- Creative and disruptive, loves embracing the challenge of rigorous testing to uncover vulnerabilities and enhance system robustness.
- Understanding of data governance principles and practices.
Preferred qualifications, capabilities, and skills
- Strong financial knowledge is preferred.
- Familiarity with Machine learning and AI paradigms such as text classification, entity recognition, information retrieval.
- Strong financial knowledge is preferred.
