Key Responsibilities

Model Development & Deployment

• Design and train advanced machine learning models, including supervised, unsupervised, and reinforcement learning models.

• Build predictive models for deal sourcing, valuation assessments, synergy identification, and competitive benchmarking.

• Deploy ML models into production systems and optimize them for performance and scalability.

Natural Language Processing (NLP)

• Develop NLP algorithms to process and analyse unstructured data, such as financial reports, press releases, and industry news.

• Build entity recognition models to extract and map companies, industries, and deal data.

• Enhance the platform's ability to summarize key insights using NLP-driven techniques.

Data Pipeline & Feature Engineering

• Collaborate with data engineers to design and manage robust pipelines for data ingestion, cleaning, and transformation.

• Engineer high-quality features to improve model performance, including dealing with noisy and incomplete data.

Predictive & Prescriptive Analytics

• Create models for trend analysis, M&A target recommendation, and risk assessment.

• Develop scoring algorithms to rank potential acquisition targets based on strategic fit, financial performance, and market dynamics.

Collaborative Problem Solving

• Work closely with product managers and domain experts to ensure AI/ML solutions align with business goals and customer needs.

• Provide technical insights and guidance to other teams on the capabilities and limitations of AI/ML technologies.

Continuous Improvement

• Stay updated with the latest developments in AI/ML, NLP, and data science to incorporate best practices into the platform.

• Conduct A/B testing and model evaluation to improve accuracy and relevance.

Scalability & Performance

• Optimize ML workflows for performance and cost-efficiency on cloud platforms like AWS or GCP.

• Ensure scalability to handle large volumes of data and multiple simultaneous users.

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Required Skills & Qualifications

Experience & Expertise

• 8+ years of professional experience in machine learning, data science, or AI engineering.

• Proven track record of deploying AI/ML solutions in production at scale.

Technical Proficiency

• Strong programming skills in Python and familiarity with libraries such as TensorFlow, PyTorch,

Scikit-learn, NumPy, and Pandas.

• Experience with NLP libraries like spaCy, Hugging Face Transformers, NLTK, or BERT models.

• Expertise in working with structured and unstructured data, including text, time-series, and tabular data.

Data Infrastructure & Tools

• Proficiency with SQL and data management systems.

• Experience with cloud-based environments (e.g., AWS SageMaker, GCP AI Platform, Azure ML).

• Familiarity with distributed computing frameworks like Apache Spark or Dask.

Mathematics & Statistics

• Strong understanding of machine learning algorithms, statistical methods, and optimization techniques.

• Knowledge of time-series forecasting, clustering, and recommendation systems.

Problem-Solving & Business Acumen

• Ability to translate complex business problems into machine learning solutions.

• Strong domain knowledge in Recommendation Engines, Matchmaking Algorithms, or Market Intelligence is a plus.

Communication & Collaboration

• Excellent communication skills to explain technical concepts to non-technical stakeholders.

• Experience working in cross-functional teams and contributing to a collaborative work environment.

Preferred Qualifications

• Hands-on experience with graph-based analytics for mapping relationships between companies, industries, or deals.

• Knowledge of data visualization tools like Tableau, Power BI, or Plotly for presenting insights.

• Experience with automated ML (AutoML) platforms to speed up experimentation and deployment.

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