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.
Qualifications:
Bachelor's or Master's degree in Computer Science, Engineering, or a related field from a reputable university, or equivalent practical experience.
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.
About You:
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!
Salary:
The annual salary for this position ranges from $105K to $125K depending on experience and qualifications.
What We Offer:
Professional growth and development opportunities.
A collaborative and innovative work environment.
A healthy work-life balance with regular working hours: 8 hours per day, 5 days a week.
