Requirements

Additional Requirements

If you have a passion for building robust, scalable data-driven solutions and a desire to thrive in a fast-paced, cloud-native environment with modern technical skills, we want to hear from you!

• 8+ years in data science, machine learning, or data engineering roles with a strong background in deploying models in production and building scalable data systems.

• Proficiency in programming languages such as Python and R, and experience with SQL and NoSQL databases. Extensive experience with ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn) and data processing frameworks (e.g., Spark, Hadoop).

• Strong foundation in statistical analysis, machine learning, and deep learning, with hands-on experience in supervised and unsupervised learning, reinforcement learning, and time-series analysis.

• Hands-on experience with cloud-based environments (AWS, Azure, GCP) and familiarity with data lake architectures, distributed computing, and big data tools.

• Knowledge of MLOps methodologies and tools (e.g., MLflow, Kubeflow) for model lifecycle management and experience in DevOps practices to ensure scalable and efficient model deployment.

• Proven ability to translate complex data into actionable insights with strong problem-solving skills and a passion for driving impact through data.

• Excellent interpersonal and communication skills to work effectively with technical and non-technical stakeholders.

• Comfortable working in an Agile/Scrum environment, collaborating with cross-functional teams.

• Excellent written and verbal communication skills in English for effective collaboration with stakeholders and team members.

• Familiarity with modern AI technologies is a plus.

Responsibilities & Context

Job Description:

As a Senior Data Science Engineer, you will be a key contributor to data-driven solutions, applying deep statistical and machine learning knowledge to optimize, innovate, and scale our data initiatives. Your role will encompass end-to-end data solutions, from developing complex models to deploying data pipelines in production, working alongside data engineers, analysts, and cross-functional teams. You will play a vital role in shaping our data strategy, advancing our AI/ML initiatives, and driving measurable outcomes across the organization.

Key Responsibilities:

• Build and optimize advanced predictive models and machine learning algorithms (e.g., NLP, deep learning, recommender systems) tailored to solve high-impact business challenges.

• Design, build, and maintain robust, scalable data pipelines and workflows using tools like Spark, Kafka, and Airflow, ensuring seamless integration and accessibility for analytics and machine learning applications.

• Manage and process large datasets from varied sources, implementing real-time data processing techniques to enable low-latency, high-volume analytics and AI models.

• Partner with product managers, software engineers, and business stakeholders to translate business requirements into data-driven solutions and identify new data product opportunities.

• Leverage cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes) to deploy, monitor, and optimize models in production, ensuring efficiency, scalability, and security.

• Establish MLOps practices to monitor model performance, retrain models, and manage the lifecycle of deployed solutions, ensuring accuracy and relevance in dynamic environments.

• Stay up-to-date with the latest trends in AI/ML, data science, and big data, leading exploratory research initiatives to keep Object Data at the forefront of technological advancements.

Location: New Jersey, USA