Data Science Lead Specialist Engineer

Job Title : Data Science Lead Specialist Engineer

Location : Delaware, Delaware

Experience Required : 12+ Years

Employment Type : Contract

Interview Type : In-Person or Webcam

Job Overview

We are seeking a highly experienced Data Science Lead Specialist Engineer with strong expertise in advanced analytics, machine learning, statistical modeling, and data engineering. The ideal candidate will be responsible for driving end-to-end data science initiatives, leading technical teams, collaborating with business stakeholders, and developing scalable AI and ML solutions that solve complex business problems.

Key Responsibilities

Lead data science strategy, architecture, design, and implementation across multiple business units.

Work closely with stakeholders to identify business opportunities, gather requirements, and translate them into analytics and ML models.

Develop predictive and prescriptive models leveraging machine learning, deep learning, NLP, and other advanced techniques.

Perform large-scale data exploration, data mining, feature engineering, and model optimization.

Design and implement scalable pipelines for model training, deployment, and monitoring in production environments.

Lead a team of data scientists, analysts, and ML engineers, providing technical mentorship and guidance.

Evaluate new technologies, tools, and frameworks to improve model performance and efficiency.

Present actionable insights and deliver executive-level data-driven recommendations.

Ensure data governance, quality assurance, security, and compliance best practices.

Required Qualifications

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field.

12+ years of experience in Data Science, Machine Learning, AI engineering, or related areas.

Strong programming skills in Python, R, Scala, and SQL.

Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, Spark MLlib.

Strong experience with cloud platforms such as AWS, Azure, or Google Cloud, including ML Ops capabilities.

Proven experience designing and deploying production-grade machine learning solutions.

Expertise in statistical modeling, predictive analytics, optimization, deep learning, and NLP techniques.

Experience working with big data technologies such as Spark, Hadoop, Hive, Kafka, Databricks.

Excellent communication skills with ability to clearly present complex analytics to non-technical stakeholders.

Leadership experience managing teams and driving enterprise-wide initiatives.

Preferred Skills

PhD in a quantitative discipline or equivalent advanced research background.

Experience in data visualization tools such as Power BI, Tableau, or Looker.

Experience with DevOps and ML Ops tools such as Docker, Kubernetes, Airflow, MLflow, Kubeflow.

Experience in Generative AI, LLM fine-tuning, and vector databases.

Experience working in regulated industries such as Finance, Healthcare, or Insurance.

Ability to architect large-scale data science platforms and automation frameworks.