
Data Scientist Leader
2 weeks ago
About our team
We're a dynamic group of professionals working together to develop and implement machine learning models using Python, SQL, and other relevant tools.
- We're looking for a seasoned Senior Machine Learning Engineer who can collaborate with cross-functional teams to identify opportunities for applying machine learning techniques to business problems.
- The ideal candidate will conduct research and experimentation to explore new machine learning approaches and technologies, optimizing existing models for improved performance and efficiency.
- They'll provide technical leadership and mentorship to junior members of the team.
Requirements
- Strong background in machine learning, natural language processing, artificial intelligence, predictive modeling, statistical modeling.
- In-depth knowledge of reinforcement learning algorithms (e.g., DQN, PPO, Actor-Critic) with hands-on project experience.
- Proven experience in time series forecasting methods (e.g., ARIMA, LSTM, Prophet) and their practical application.
- Proficient in Python, SQL, and other relevant programming languages.
- Familiarity with cloud computing platforms such as AWS or Azure is a plus.
- Ability to work collaboratively in a dynamic environment and communicate effectively with stakeholders at all levels of the organization.
- Bachelor's or master's degree in computer science, mathematics, statistics, or related field with a minimum of 5 years of experience.
Key Responsibilities:
- Develop and implement machine learning models using Python, SQL, and other relevant tools.
- Collaborate with cross-functional teams to identify opportunities for applying machine learning techniques to business problems.
- Conduct research and experimentation to explore new machine learning approaches and technologies.
- Optimize existing machine learning models for improved performance and efficiency.
- Provide technical leadership and mentorship to junior members of the team.
Tech Requirements
- Python.
- SQL.
- Reinforcement learning algorithms.
- Time series forecasting methods.
- Cloud computing platforms.
If you have experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space, that's great, but not required.
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