Job Description:
As the Lead Data Scientist, you will spearhead our data science initiatives, guiding a team of data scientists and collaborating with cross-functional teams to design and implement data-driven solutions. You will utilize your engineering background and AWS cloud experience to optimize data processes, build scalable models, and ensure the robustness of our data infrastructure.
Key Responsibilities:
- Team Leadership: Mentor and lead a team of data scientists, fostering a collaborative environment that encourages innovation and professional growth.
- Data Strategy: Develop and execute data strategies that align with business objectives, ensuring the effective use of data for decision-making.
- Model Development: Design, build, and deploy predictive models and machine learning algorithms that drive business outcomes.
- Cloud Infrastructure: Utilize AWS services (e.g., S3, Redshift, SageMaker) to develop and maintain scalable data pipelines and architectures.
- Data Engineering: Collaborate with data engineers to streamline data collection, storage, and processing, ensuring high-quality data for analysis.
- Performance Optimization: Monitor and enhance model performance, deploying techniques such as hyperparameter tuning and model retraining.
- Cross-Functional Collaboration: Work closely with product managers, software engineers, and business stakeholders to understand requirements and deliver impactful data solutions.
- Communication: Present findings and insights to technical and non-technical stakeholders, translating complex concepts into actionable recommendations.
Qualifications:
- Education: Master’s or PhD in Computer Science, Data Science, Statistics, or a related field.
- Experience:
- 5+ years of experience in data science or analytics, with at least 2 years in a leadership role.
- Strong engineering background with proficiency in programming languages such as Python, R, or Scala.
- Hands-on experience with AWS cloud services and data engineering tools.
- Technical Skills:
- Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Proficient in data visualization tools (e.g., Tableau, Power BI, Matplotlib).
- Solid understanding of data processing frameworks (e.g., Apache Spark, Hadoop).
- Problem-Solving: Strong analytical and problem-solving skills, with the ability to work with complex data sets and develop innovative solutions.
- Communication Skills: Excellent verbal and written communication skills, with the ability to convey technical information to a non-technical audience.
