Finance Subject Matter Expert-SME
Hello Everyone,
Greetings from the Datamundi team!!
We are looking for Finance Subject Matter Experts.
Please find the details below.
Scope of work-Provide finance domain expertise to train and evaluate AI models by developing financial workflows, validating datasets, assessing model outputs, and ensuring accurate analysis across valuation, investment research, financial modeling, and due diligence use cases.
Job Description- Finance Expert
As a Finance Domain Expert, you will be responsible for the following key tasks:
⢠Workflow Definition: Collaborate with our AI and engineering teams to define the most common research-based workflows within your specific domain (e.g., M&A deal analysis in Investment Banking, company valuation in Equity Research, portfolio construction in Asset Management).
⢠Evaluation Creation: Develop and create comprehensive evaluations and test cases to assess the completion of these defined workflows. This involves specifying inputs, expected outputs, and the criteria for success.
⢠Capability Assessment: Design evaluations to assess the key capabilities of the LLM in handling complex financial tasks, including data extraction, financial statement analysis, market trend identification, and risk assessment.
⢠Data Curation & Annotation: Provide guidance on identifying and curating high-quality financial data and content that is essential for training and fine-tuning the LLM.
⢠Feedback & Iteration: Work closely with the product team to provide constructive feedback on the LLM's performance and suggest improvements to enhance its accuracy and relevance.
Key Requirements
⢠Domain Expertise: 12+ years of practical, professional experience in one of the following fields:
⢠Investment Banking: Experience with M&A, capital markets, or corporate finance.
⢠Private Equity: Experience with deal sourcing, due diligence, and portfolio management.
⢠Asset Management: Experience with portfolio management, investment strategy, or quantitative analysis.
⢠Equity Research: Experience with company analysis, financial modeling, and report writing.
⢠Experience : Entry level, Mid level and Executive level professionals
⢠Analytical Skills: Strong analytical and critical thinking abilities to break down complex financial processes into logical, measurable steps.
⢠Communication: Excellent communication skills and the ability to articulate complex financial concepts to a non-finance audience.
⢠Technical Acumen : A basic understanding of AI, machine learning, or experience working with technical teams is a significant plus.
⢠Working experience on AI/LLM financial models to build fintech applications and exposure.
⢠Ruberics, Data annotations and prompt engineering knowledge which can be applied on products and services to eusure quality services and desired outputs.
⢠Attention to Detail: Meticulous attention to detail is crucial for creating accurate and robust evaluation criteria.
Private Equity | Market & Commercial Diligence
⢠Definition: Deep-dive workflows that size a targetâs market, analyze competitive dynamics, test growth drivers, and capture customer / channel insights to supportâor challengeâan investment thesis.
⢠Data input types: Public filings (10-K/annual reports), industry research databases (PitchBook, CapIQ, Gartner, IB reports), consultant decks, company websites & investor presentations, expert-network call notes/transcripts, press releases, government statistics (e.g., Census, BLS).
Asset Management | Earnings Analysis & Quarter Updates
⢠Definition: Fast-turn workflows that digest a companyâs quarterly results, benchmark them against expectations, and refresh internal views or models ahead ofâor immediately afterâan earnings event.
⢠Data input types: Company earnings release, slide deck, SEC 10-Q/8-K, earnings-call transcript, sell-side consensus, management follow-up notes, and models.
Investment Banking | Market & Competitive Research
⢠Definition: Sector-intelligence workflows that size an industry, map competitors, highlight secular trends, and position a client (or target) within that landscape so bankers can craft compelling pitches, valuations, and strategic recommendations.
⢠Data input types: Public filings & earnings call transcripts (10-K/10-Q/20-F, S-1), industry and equity-research reports, company websites & investor presentations, product review sites, press releases and trade-press coverage, government statistics (Census, BLS), social-media/forums sentiment, expert-network call transcripts.
Equity Research | Investment Thesis & Research
⢠Definition: Analytical workflows that develop, update, or challenge an equity investment viewâtypically a buy/hold/sell thesisâby integrating financial modeling, valuation, and industry analysis to inform real-time or near-term trading decisions.
⢠Data input types: Company filings (10-K, 10-Q, earnings releases), valuation multiples (P/E, EV/EBITDA), comp tables, earnings call and investor day transcripts, industry KPIs and macro indicators, analyst consensus, financial news, financial models (DCF, scenario cases), expert and investor relations calls, portfolio exposure data, trading desk commentary.
Domains / Workflows
⢠Discounted Cash Flow (DCF) Analysis
⢠Leveraged Buyout (LBO) Modeling
⢠Mergers & Acquisitions (M&A) Modeling
⢠Comparable & Precedent Transaction Analysis
⢠Financial Forecasting
Greetings from the Datamundi team!!
We are looking for Finance Subject Matter Experts.
Please find the details below.
Scope of work-Provide finance domain expertise to train and evaluate AI models by developing financial workflows, validating datasets, assessing model outputs, and ensuring accurate analysis across valuation, investment research, financial modeling, and due diligence use cases.
Job Description- Finance Expert
As a Finance Domain Expert, you will be responsible for the following key tasks:
⢠Workflow Definition: Collaborate with our AI and engineering teams to define the most common research-based workflows within your specific domain (e.g., M&A deal analysis in Investment Banking, company valuation in Equity Research, portfolio construction in Asset Management).
⢠Evaluation Creation: Develop and create comprehensive evaluations and test cases to assess the completion of these defined workflows. This involves specifying inputs, expected outputs, and the criteria for success.
⢠Capability Assessment: Design evaluations to assess the key capabilities of the LLM in handling complex financial tasks, including data extraction, financial statement analysis, market trend identification, and risk assessment.
⢠Data Curation & Annotation: Provide guidance on identifying and curating high-quality financial data and content that is essential for training and fine-tuning the LLM.
⢠Feedback & Iteration: Work closely with the product team to provide constructive feedback on the LLM's performance and suggest improvements to enhance its accuracy and relevance.
Key Requirements
⢠Domain Expertise: 12+ years of practical, professional experience in one of the following fields:
⢠Investment Banking: Experience with M&A, capital markets, or corporate finance.
⢠Private Equity: Experience with deal sourcing, due diligence, and portfolio management.
⢠Asset Management: Experience with portfolio management, investment strategy, or quantitative analysis.
⢠Equity Research: Experience with company analysis, financial modeling, and report writing.
⢠Experience : Entry level, Mid level and Executive level professionals
⢠Analytical Skills: Strong analytical and critical thinking abilities to break down complex financial processes into logical, measurable steps.
⢠Communication: Excellent communication skills and the ability to articulate complex financial concepts to a non-finance audience.
⢠Technical Acumen : A basic understanding of AI, machine learning, or experience working with technical teams is a significant plus.
⢠Working experience on AI/LLM financial models to build fintech applications and exposure.
⢠Ruberics, Data annotations and prompt engineering knowledge which can be applied on products and services to eusure quality services and desired outputs.
⢠Attention to Detail: Meticulous attention to detail is crucial for creating accurate and robust evaluation criteria.
Private Equity | Market & Commercial Diligence
⢠Definition: Deep-dive workflows that size a targetâs market, analyze competitive dynamics, test growth drivers, and capture customer / channel insights to supportâor challengeâan investment thesis.
⢠Data input types: Public filings (10-K/annual reports), industry research databases (PitchBook, CapIQ, Gartner, IB reports), consultant decks, company websites & investor presentations, expert-network call notes/transcripts, press releases, government statistics (e.g., Census, BLS).
Asset Management | Earnings Analysis & Quarter Updates
⢠Definition: Fast-turn workflows that digest a companyâs quarterly results, benchmark them against expectations, and refresh internal views or models ahead ofâor immediately afterâan earnings event.
⢠Data input types: Company earnings release, slide deck, SEC 10-Q/8-K, earnings-call transcript, sell-side consensus, management follow-up notes, and models.
Investment Banking | Market & Competitive Research
⢠Definition: Sector-intelligence workflows that size an industry, map competitors, highlight secular trends, and position a client (or target) within that landscape so bankers can craft compelling pitches, valuations, and strategic recommendations.
⢠Data input types: Public filings & earnings call transcripts (10-K/10-Q/20-F, S-1), industry and equity-research reports, company websites & investor presentations, product review sites, press releases and trade-press coverage, government statistics (Census, BLS), social-media/forums sentiment, expert-network call transcripts.
Equity Research | Investment Thesis & Research
⢠Definition: Analytical workflows that develop, update, or challenge an equity investment viewâtypically a buy/hold/sell thesisâby integrating financial modeling, valuation, and industry analysis to inform real-time or near-term trading decisions.
⢠Data input types: Company filings (10-K, 10-Q, earnings releases), valuation multiples (P/E, EV/EBITDA), comp tables, earnings call and investor day transcripts, industry KPIs and macro indicators, analyst consensus, financial news, financial models (DCF, scenario cases), expert and investor relations calls, portfolio exposure data, trading desk commentary.
Domains / Workflows
⢠Discounted Cash Flow (DCF) Analysis
⢠Leveraged Buyout (LBO) Modeling
⢠Mergers & Acquisitions (M&A) Modeling
⢠Comparable & Precedent Transaction Analysis
⢠Financial Forecasting