Lead Data Scientist
@ Enable Data Incorporated

Hybrid
Hybrid
Posted 2 days ago

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XXXXXXXXX XXXXXXXXX XXXXXXX****** @enabledata.com
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Job Details

About the Role

Enable Data Incorporated is looking for a talented and experienced Lead Data Scientist to join our innovative team. In this role, you will drive data science initiatives, develop predictive models, and provide insights that guide key business decisions.

Responsibilities

Collaborate with cross-functional teams, including engineering, product management, and business stakeholders to design and implement advanced analytics solutions that optimize operations and enhance product offerings.

Required Qualifications

  • 7+ years of experience in data science or related field
  • Expertise in machine learning, predictive modeling, and statistical analysis
  • Proficiency in Python and libraries such as Pandas, Numpy, scikit-learn, TensorFlow, and Keras
  • Experience with Databricks and SQL
  • Familiarity with Generative AI solutions and LLMs like GPT, Claude, and LLaMA
  • Knowledge of libraries such as LangChain, Transformers, OpenAI SDK, or Hugging Face
  • Hospitality experience is preferred

Key Skills/Competency

Lead Data Scientist, Statistical Analysis, Machine Learning, Predictive Modeling, Big Data, Python, Databricks, SQL, Generative AI, Advanced Analytics

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How to Get Hired at Enable Data Incorporated

🎯 Tips for Getting Hired

  • Customize Resume: Tailor resume highlighting ML and analytics.
  • Research Enable Data Incorporated’s culture: Understand mission and recent projects.
  • Practice Technical Interviews: Prepare for coding and data challenges.
  • Network Strategically: Connect with team members on LinkedIn.

đź“ť Interview Preparation Advice

Technical Preparation

Review Python coding and ML libraries.
Study Databricks and SQL query optimization.
Practice building predictive models with real data.
Refresh knowledge on generative AI techniques.

Behavioral Questions

Discuss cross-functional collaboration experience.
Describe conflict resolution within teams.
Explain leadership style in data projects.
Share examples of handling tight deadlines.

Frequently Asked Questions