Work closely with cross-functional teams to understand business requirements and identify opportunities for applying data science and machine learning techniques.

Design and develop data science models and algorithms that solve complex business problems and deliver actionable insights.

Implement end-to-end data science solutions, including data collection, preprocessing, feature engineering, model training, evaluation, and deployment.

Collaborate with data engineers to ensure the availability, quality, and reliability of data for analysis and modeling.

Conduct exploratory data analysis to gain insights and identify patterns, trends, and anomalies in the data.

Develop and deploy scalable machine learning models and algorithms that can handle large-scale datasets and real-time data streams.

Optimize and fine-tune models for performance, accuracy, and scalability.

Collaborate with software engineers to integrate data science models and algorithms into production systems and applications.

Stay up to date with the latest advancements in data science, machine learning, and artificial intelligence and apply them to solve business challenges.

Communicate and present complex technical concepts and findings to both technical and non-technical stakeholders in a clear and understandable manner.

Collaborate with data scientists and domain experts to understand the domain-specific challenges and requirements and translate them into data science solutions.

Bachelor's or master's degree in computer science, data science, statistics, or a related field.

Proven experience as a Data Science Engineer or in a similar role, with a focus on developing and deploying data science models and applications.

Strong programming skills in languages such as Python or R, with experience in data manipulation, statistical analysis, and machine learning libraries (e.g., Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).

Proficiency in SQL and experience with relational databases for data retrieval and manipulation.

Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.

Experience with big data technologies and distributed computing frameworks such as Hadoop and Spark.

Familiarity with cloud platforms and services, such as AWS, Azure, or Google Cloud, and their data-related services (e.g., S3, EMR, ML services).

Strong knowledge of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and natural language processing.

Familiarity with software development practices and version control systems (e.g., Git).

Excellent problem-solving and analytical abilities, with keen attention to detail.

Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and present complex concepts to non-technical stakeholders.