Salesforce
Senior Data Science Engineer - Hyperforce Solutions Engineering
Salesforce, Washington, District of Columbia, us, 20022
About SalesforceWe’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.Senior Data Science EngineerWe're Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you've come to the right place. Join us if you are interested in developing data science solutions to make a meaningful impact in improving developer productivity! We are looking to hire experienced data scientists with a strong sense of service ownership, ability to contribute to design and quickly absorb solutions, empathy for customers, excellent communication skills, a drive for continuous improvement. The role will entail overseeing the entire lifecycle of data-science and ML solutions from POC to deployment to ongoing maintenance and enhancement. Our team creates and supports several existing ML products such as a customer support generative AI chatbot, semantic search, error identification, customer support dashboards, etc. The Hyperforce Solutions Engineering Platform Team is distributed across North America. This role will be based in the Bay Area.ResponsibilitiesLLM Application Development: Lead the development and implementation of LLM applications using LangChain, focusing on transformers and GPT architecture. Understand and apply the workings of LLM, Prompt Engineering, RAG, and Agent concepts to enhance application functionalities.Developing conversational AI agents, chatbots, or virtual assistants.Integrate AI agents into existing software systems and platforms, ensuring seamless operation and compatibility.Experimentation and Testing: Design and conduct A/B testing for various experiments and improvements. Utilize statistical tools to analyze test results and make data-driven decisions to optimize application performance.Data Analysis and Visualization: Develop and maintain dashboards using tools like Tableau and Splunk to visualize experiment results, making insights accessible to Business stakeholders.Programming: Exhibit proficiency in Python and SOQL for data manipulation, analysis, and automation tasks. Ensure code quality and maintainability.Research and Development: Stay up-to-date with the latest industry trends, including llamaindex, mistral, LLAMA3, and other emerging LLMs. Evaluate and incorporate new technologies and methodologies to maintain our competitive edge.Collaboration and Leadership: Work closely with cross-functional teams to identify opportunities for improvement and innovation. Mentor junior data scientists and contribute to the team's knowledge sharing and skill development.Required Skills (Data Scientist)
A Master's degree in Computer Science, Computer Engineering, Statistics, Data Science or other relevant coursesMinimum of 5 years of experience in a data science role, with a proven track record of developing and implementing LLM applications.Demonstrated experience with Deep learning concepts and NLP frameworks such as nltk, pytorch, tensorflow, keras, huggingface, sentence_transformers, etc.Deep understanding of transformers, GPT architecture, LangChain functionalities, RAG, and Agent concepts.Experience with RAG, Chat Agents and content personalization techniquesExtensive experience in A/B testing, statistical analysis and data visualization tools like Tableau and Splunk.Experience with ML concepts and statistical libraries like pandas, matplotlib, numpy, sklearn (or their equivalents) is requiredDemonstrated ease with querying languages like SQL (SOQL / SAQL is a plus)Proficiency in handling unstructured dataIn-depth knowledge of NoSQL databases (e.g., MongoDB, Cassandra)Experience with vector databases (e.g., Pinecone, Weaviate)Familiarity with knowledge graphs (e.g., Neo4j, RDF)Familiarity with the latest industry trends and advancements in data science and machine learning. Up-to-date with the latest LLM trends and fine-tuning methods.Strong knowledge of LLM fine-tuning methods such as PEFT, QLORA, and LORA and prompt engineering.Excellent communication and leadership skills, ability to work in a team environment, strong problem-solving abilities, and a proactive approach to tackling challenges.Develop and integrate REST APIs to facilitate seamless communication between our LLM applications and other systems or services.Ability to learn fast, work collaboratively, and respond to broad problem statements in a data-driven mannerAble to structure long-term project roadmaps, resolve inter-team dependencies, and work with stakeholders and ensure timely delivery of project milestones and deliverables.Demonstrated experience with ETL pipelines and workflows - Jenkins / Airflow / Terraform any other ETL orchestration frameworkPrior experience of deploying ML applications on cloud ecosystems like AWS or GCP, and monitoring / enhancing systems in an ongoing fashion Experience with data modeling on RDS / DynamoDB / Hive etc.Experience with Kubernetes for deploying and managing scalable ML models.
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A Master's degree in Computer Science, Computer Engineering, Statistics, Data Science or other relevant coursesMinimum of 5 years of experience in a data science role, with a proven track record of developing and implementing LLM applications.Demonstrated experience with Deep learning concepts and NLP frameworks such as nltk, pytorch, tensorflow, keras, huggingface, sentence_transformers, etc.Deep understanding of transformers, GPT architecture, LangChain functionalities, RAG, and Agent concepts.Experience with RAG, Chat Agents and content personalization techniquesExtensive experience in A/B testing, statistical analysis and data visualization tools like Tableau and Splunk.Experience with ML concepts and statistical libraries like pandas, matplotlib, numpy, sklearn (or their equivalents) is requiredDemonstrated ease with querying languages like SQL (SOQL / SAQL is a plus)Proficiency in handling unstructured dataIn-depth knowledge of NoSQL databases (e.g., MongoDB, Cassandra)Experience with vector databases (e.g., Pinecone, Weaviate)Familiarity with knowledge graphs (e.g., Neo4j, RDF)Familiarity with the latest industry trends and advancements in data science and machine learning. Up-to-date with the latest LLM trends and fine-tuning methods.Strong knowledge of LLM fine-tuning methods such as PEFT, QLORA, and LORA and prompt engineering.Excellent communication and leadership skills, ability to work in a team environment, strong problem-solving abilities, and a proactive approach to tackling challenges.Develop and integrate REST APIs to facilitate seamless communication between our LLM applications and other systems or services.Ability to learn fast, work collaboratively, and respond to broad problem statements in a data-driven mannerAble to structure long-term project roadmaps, resolve inter-team dependencies, and work with stakeholders and ensure timely delivery of project milestones and deliverables.Demonstrated experience with ETL pipelines and workflows - Jenkins / Airflow / Terraform any other ETL orchestration frameworkPrior experience of deploying ML applications on cloud ecosystems like AWS or GCP, and monitoring / enhancing systems in an ongoing fashion Experience with data modeling on RDS / DynamoDB / Hive etc.Experience with Kubernetes for deploying and managing scalable ML models.
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