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At PitchBook, Morningstar we are always looking forward.
We continue to innovate, evolve, and invest in ourselves to bring out the best in everyone. We're deeply collaborative and thrive on the excitement, energy, and fun that reverberates throughout the company. Our extensive learning programs and mentorship opportunities help us create a culture of curiosity that pushes us to always find new solutions and better ways of doing things. The combination of a rapidly evolving industry and our high ambitions means there's going to be some ambiguity along the way, but we excel when we challenge ourselves. We're willing to take risks, fail fast, and do it all over again in the pursuit of excellence.
If you have a good attitude and are willing to roll up your sleeves to get things done, PitchBook is the place for you.
About the Role:
As a member of the Product and Engineering team at PitchBook, you will be part of a team of big thinkers, innovators, and problem solvers who strive to deepen the positive impact we have on our customers and our company every day. We value curiosity and the drive to find better ways of doing things. We thrive on customer empathy, which remains our focus when creating excellent customer experiences through product innovation. We know that greatness is achieved through collaboration and diverse points of view, so we work closely with partners around the globe. As a team, we assume positive intent in each other's words and actions, value constructive discussions, and foster a respectful working environment built on integrity, growth, and business value. We invest heavily in our people, who are eager to learn and constantly improve. Join our team and grow with us!
PitchBook's Data Science and Machine Learning team has a clear mission: leverage cutting-edge machine learning and cloud technologies to automatically research private markets and improve the navigability of our platform. The Machine Learning (ML) Engineer is responsible for using machine learning and natural language processing (NLP) to collect a high-volume of data for the PitchBook Platform and surface insights for our users. This role requires a strong desire to learn and deliver value through scalable machine learning systems. A strong motivation to succeed is critical and everyone has the opportunity to shape the long-term direction of our team.
Primary Job Responsibilities:
Skills and Qualifications:
Working Conditions:
Morningstar India is an equal opportunity employer
Morningstar's hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week.
Morningstar, Inc. is a leading provider of independent investment insights in North America, Europe, Australia, and Asia. The Company offers an extensive line of products and solutions that serve a wide range of market participants, including individual and institutional investors in public and private capital markets, financial advisors and wealth managers, asset managers, retirement plan providers and sponsors, and issuers of fixed-income securities. Morningstar provides data and research insights on a wide range of investment offerings, including managed investment products, publicly listed companies, private capital markets, debt securities, and real-time global market data. Morningstar also offers investment management services through its investment advisory subsidiaries, with approximately $328 billion in AUMA as of Sept. 30, 2024. The Company operates through wholly-owned subsidiaries in 32 countries.
Job ID: 108512963
Skills:
Machine Learning, Kafka, Sql, Tensorflow, Pandas, Numpy, Gcp, Pytorch, Docker, Kubernetes, Python, AWS, LangChain, Airflow, scikit-learn
Skills:
Kubernetes, python, Tensorflow, Pytorch, aws, mlflow
Skills:
BigQuery, Machine Learning, Natural Language Processing, Deep Learning, Tensorflow, MLops, Docker, XGBoost, Flask, Keras, Python, Scikit-learn, GCP Vertex AI, Gemini, CatBoost
Skills:
vectors , Restful Apis, API design, Java, Python, Graphql, Google Cloud Platform, Knowledge graph, custom connectors, Vertex AI, Oauth Flows, MCP connections, Go, Indexing, GRPC, observability tools, RAG, Embeddings, Agentic AI, Generative AI
Skills:
Python, Kubernetes, Github, MLOps framework, Big Query SQL, Vertex AI, Airflow, Kubeflow, Docker-Container, GCP Services
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