Lead Data Scientist

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.