Solution Architect

Amdocs-posted about 1 year ago
Full-time • Senior
Bellevue, WA
10,001+ employees
Professional, Scientific, and Technical Services
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The Solution Architect at Amdocs is responsible for driving advanced data science and machine learning solutions, leveraging AI expertise to deliver impactful business insights and innovative models. This role involves analyzing large datasets, developing machine learning algorithms, and collaborating with cross-functional teams to support data-driven business decisions.

  • Analyze and model large datasets using advanced statistical and machine learning techniques to uncover actionable insights.
  • Develop and implement innovative machine learning algorithms to solve complex business problems and optimize processes.
  • Engineer and select key features from datasets to improve model performance.
  • Create and present data visualizations and reports to communicate insights effectively to stakeholders.
  • Collaborate with cross-functional teams to understand business needs and set project goals.
  • Mentor junior team members and foster a culture of continuous learning within the team.
  • Evaluate and optimize machine learning models for accuracy, scalability, and efficiency.
  • Ensure data governance policies and compliance with security and privacy standards throughout the data lifecycle.
  • Stay abreast of the latest advancements in data science, machine learning, and AI.
  • Master's degree in computer science, Statistics, Mathematics, or a related field.
  • At least five years of professional experience in data science or machine learning.
  • Strong proficiency in Python, R, or Scala, with hands-on experience in data manipulation and analysis libraries such as Pandas, NumPy, and SciPy.
  • Expertise in machine learning techniques including regression, classification, clustering, ensemble methods, and time-series forecasting.
  • Experience with deep learning frameworks like TensorFlow or PyTorch.
  • Strong knowledge of feature engineering, hyperparameter tuning, and model deployment practices.
  • Hands-on experience with AI techniques, including natural language processing (NLP) and computer vision (CV).
  • Familiarity with generative AI models and libraries for data science applications.
  • Knowledge of AI model lifecycle management, including monitoring and retraining.
  • Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.
  • Background in telecom domains such as RF/OSS data analysis, network optimization, or customer analytics.
  • Health insurance
  • Dental insurance
  • Paid time off
  • Parental leave
  • Vision insurance
  • Life insurance
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