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AI Data Science Associate Director

2 months ago

Columbia, Maryland, United States Blend360 Full time
About Blend360

Blend360 is a world-class marketing, analytics, and technology company that delivers exceptional results for our clients. Our primary focus is Data Sciences, leveraging data and applied mathematics to solve complex business challenges. We're known for our exceptional people, our get-it-done mentality, and delivering high-impact, sustainable results.

Job Description

We're seeking an experienced AI Data Science Associate Director to lead our team of data scientists in providing AI Data Science solutions to our clients. As a key member of our global Data Science team, you'll work closely with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints.

Key Responsibilities
  • Lead a Team of Data Scientists: Oversee a team of data scientists to deliver AI Data Science solutions to our clients.
  • Provide Data-Driven AI Solutions: Work with our global Data Science team to provide data-driven AI solutions for our customers using state-of-the-art methods and tools.
  • Understand Business Problems: Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints.
  • Align Approaches and Deliverables: Work with practice leaders to get stakeholder feedback, alignment on approaches, deliverables, and roadmaps.
  • Create Efficient Data Pipelines: Create and maintain efficient data pipelines, often within clients' architecture, using SQL, Spark, and Cloud Big Data technologies.
  • Assemble Complex Data Sets: Assemble large, complex data sets from client and external sources that meet functional business requirements.
  • Build Analytics Tools: Build analytics tools to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
  • Perform Data Cleaning and Integration: Perform data cleaning/hygiene, data QC, and integrate data from both client internal and external data sources on Advanced Data Science Platform.
  • Apply Deep Learning Principles: Utilize deep learning principles and architectures, including CNNs, RNNs, and transformers, to apply these techniques to natural language processing tasks.
  • Manipulate Model Parameters: Manipulate model parameters to achieve desired outcomes in text generation.
  • Craft Effective Prompts: Craft effective prompts that guide AI models to generate desired outputs. Understand how different prompt structures influence AI behavior.
  • Integrate External Knowledge Sources: Use RAG models, which combine a retrieval component with a generator to enhance the quality and relevance of the AI's output. Understand how to effectively integrate external knowledge sources into AI responses.
  • Train and Fine-Tune Models: Train and fine-tune models on specific datasets to improve performance and ensure the relevance of the outputs to the task at hand.
  • Conduct Statistical Data Analysis: Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making.
  • Document Predictive Models: Document predictive models/machine learning results that can be incorporated into client-deliverable documentation.
  • Deploy Models and Algorithms: Assist clients to deploy models and algorithms within their own architecture.
Qualifications
  • Consulting Experience: Proven experience in consulting and leading teams.
  • Leadership Skills: Proven ability to lead a team and drive business impact.
  • Deep Learning Expertise: Profound knowledge of deep learning principles and architectures, including CNNs, RNNs, and transformers, with the ability to apply these techniques to natural language processing tasks.
  • Deployment Experience: Understanding of how to integrate models into production.
  • Engineering Experience: In-depth understanding of the workings of LLMs and the ability to manipulate model parameters to achieve desired outcomes in text generation.
  • Prompt Crafting Expertise: Expertise in crafting effective prompts that guide AI models to generate desired outputs. Understand how different prompt structures influence AI behavior.
  • RAG Model Experience: Experience with RAG models, which combine a retrieval component with a generator to enhance the quality and relevance of the AI's output. Understand how to effectively integrate external knowledge sources into AI responses.
  • Model Training and Fine-Tuning: Capability to train and fine-tune models on specific datasets to improve performance and ensure the relevance of the outputs to the task at hand.
  • MS Degree in Quantitative Field: MS degree in Statistics, Math, Data Analytics, or a related quantitative field.
  • Post-Graduate Experience: At least 5 years of post-graduate professional experience in Advanced Data Science, such as predictive modeling, statistical analysis, machine learning, text mining, geospatial analytics, time series forecasting, optimization.
  • NLP and AI Experience: Demonstrated experience with NLP and other components of AI.
  • AI Solution Implementation: Experience implementing AI solutions.
  • Advanced Data Science Software: Experience with one or more Advanced Data Science software languages (Python, R, SAS).
  • Machine Learning Deployment: Proven ability to deploy machine learning models from the research environment (Jupyter Notebooks) to production via procedural or pipeline approaches.
  • SQL and Relational Databases: Experience with SQL and relational databases, query authoring and tuning as well as working familiarity with a variety of databases including Hadoop/Hive.
  • Spark and Data-Frames: Experience with Spark and data-frames in PySpark or Scala.
  • Cloud-Based Platforms: Comfortable with cloud-based platforms (AWS, Azure, Google).
  • Google Analytics and Adobe Analytics: Experience with Google Analytics, Adobe Analytics, Optimizely a plus.