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Data Analyat / Technical Product

Black Ore is building the leading AI platform for financial services. By combining LLMs, proprietary AI/ML and automation we accelerate core workflows for the industry, allow financial services professionals to be more productive and enable consumers to enhance their personal finance. Our flagship product, Tax Autopilot, combines AI with federal and state tax codes & regulations to simplify the tax preparation and review process for Certified Public Accountants (CPAs) and accounting firms.

Founded in 2022, we launched with $60 million in early stage funding from some of the world’s leading investors including a16z, Founders Fund, General Catalyst, Khosla Ventures, Oak HC/FT, Trust Ventures and leading tech founders/angel investors including Jason Gardner (Founder and CEO of Marqeta), Max Levchin (Founder of Paypal and Affirm), Tom Glocer (Former CEO of Thomson Reuters), Gokul Rajaram, and Mark Britto (EVP, CPO, PayPal).

Our team has an incredibly ambitious vision to completely transform the way businesses and consumers interact in financial services. We’re looking to hire strong team members to grow the team. Some of the traits we look for are:

  • Owner Mentality – Desire to take initiative, identify problems and implement solutions
  • Mission Driven – Passion for building AI/ML solutions that reimagine how businesses and consumers operate
  • Intellectually Curious – Excitement going deep and building detailed understanding of the function, role, customer and problem space
  • Team Oriented – Ability to collaborate respectfully and put the team above the self

Minimum 5 years of experience. Can be in Israel, Austin, NY or remote. Like details and to meticulously organize spreadsheets.

Must have:

  • Affinity for Data Organization: Natural fondness for structuring information. Enthusiasm for organizing, analyzing, and presenting data effectively. Appreciation for clear, accessible data representation.
  • Data Analysis Techniques: Profound knowledge in techniques to organize, filter, and structure large datasets. Ability to standardize complex data into a manageable format.
  • Data Modeling: Conceptualization and development of data models for diverse financial documents. Ability to represent information in a normalized format accurately.
  • Database Management: Familiarity with database technologies and design principles. Understanding of normalization rules to reduce redundancy and improve data integrity.
  • Exceptional Attention to Detail: Meticulousness to ensure all variations in data are accounted for. Diligence to prevent loss or misrepresentation of critical information.
  • Analytical Mindset: Strong organizational skills and thoroughness. Ability to dissect complex problems and analyze data from various angles. Derivation of logical, efficient solutions.
  • Methodical Problem-Solving: Systematic approach to addressing and solving problems.
    Patience and Perseverance: Ability to perform repetitive tasks with high accuracy. Persistence and diligence in refining models to accurately reflect data.
  • Adaptability: Openness to new information and flexibility to adjust models and approaches. Ability to adapt to new data types or challenges as they arise.
  • Critical Thinking: Capacity to question assumptions and existing models. Ability to propose enhancements for increased accuracy and efficiency.
  • Collaborative Projects: Experience in multidisciplinary teams. Ability to translate technical details into actionable insights for diverse team members.
    Continuous Learning: Track record of learning and applying new techniques or technologies. Commitment to improving data analysis and normalization processes.

Very nice to have:

  • Programming and Scripting: Proficiency in programming languages, notably Python. Skilled in using regular expressions for pattern matching and data extraction.
  • Experience with Financial Documents: Prior work involving financial statements, reports, or similar documents. Understanding of common structures and contents in financial documents.
  • Data Cleaning and Preprocessing: Hands-on experience with cleaning and preprocessing data. Ability to remove errors, fill in missing values, and correct inconsistencies.
  • Project Involvement: Participation in projects requiring organization and structuring of unstructured or semi-structured data.
  • Financial Acumen: Understanding of financial concepts and terminologies.
  • Knowledge of the structure of financial reports to ensure data retains its original meaning and relevance.
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