Join Dorcas, a company focused on enhancing daily life decisions through technology, as a Data Scientist or Deep Learning Engineer. This remote role involves building an MVP for a recommendation system, developing causal inference methods, and implementing machine learning algorithms. Core responsibilities include designing recommendation algorithms, collaborating with cross-functional teams, and ensuring system scalability. The position offers competitive compensation and benefits.
Ideal candidates will have a strong background in Computer Science and Statistics, experience with machine learning algorithms, and proficiency in Python or R. A Master's or Ph.D. in a related field is required, along with skills in pattern recognition, causal inference, and excellent communication abilities. Experience with recommender systems is advantageous.
About Dorcas
Dorcas is being a recommendation system idea now. Our goal is to accelerate the development of recommender systems to enhance best daily life decisions. We believe in the power of technology and innovation to connect individuals and provide personalized recommendations.
Role Description
This job is going to build MVP (minimum viable product). The Data scientist will be responsible for developing causal inferences methods to estimate the effect of treatment and implementing machine learning algorithms, pattern recognition. They will also work on data analysis, statistical modeling, and algorithm optimization. The Machine Learning Engineer will collaborate with a team of engineers and data scientists to improve recommender systems.
As a Deep Learning Engineer, you will work with Data Scientist in causal inference to build recommendation system, where an end-to-end AI system processes raw sensor input to make direct decisions, we are building end-to-end AI based recommendation algorithms. These algorithms aim to discover the most compelling content on the platform — including posts, promoted content, creators, and videos — based directly on users' activity on the platform. As a key driver of this effort, you will test state-of-the-art methodologies to significantly enhance use users' experiences on the platform.
Your responsibilities will include:
- Designing and architecting recommendation algorithms
- Collaborating with cross-functional teams to integrate machine learning models into our platform
- Iterating and improving the algorithm by gathering user feedback in real time through experimentation
- Ensuring scalability and efficiency of machine learning systems
- Mentoring junior engineers and contributing to the team's growth
- Staying updated on Machine Learning and Deep Learning industry trends
Qualifications
- Strong background in Computer Science and Statistics
- Experience in developing and implementing machine learning algorithms
- Knowledge of pattern recognition and Causal inference
- Proficiency in programming languages such as Python or R
- Familiarity with algorithms and data structures
- Strong analytical and problem-solving skills
- Excellent communication and collaboration abilities
- Master's or Ph.D. in Computer Science, Engineering, or related field
- Experience with recommender systems is a plus
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