Staff-level Data Scientist

Responsibilities:

● Lead end-to-end data science projects, including problem formulation, data collection,

cleaning, feature engineering, model development, validation, and deployment.

● Apply advanced statistical analysis, machine learning algorithms, and data mining techniques

to extract insights and patterns from large-scale structured and unstructured data sets.

● Collaborate with stakeholders to define project objectives, deliverables, and success metrics

aligned with business goals.

● Develop and maintain scalable and efficient data pipelines, ensuring data integrity, quality, and

security.

● Implement and optimize machine learning models, deep learning architectures, and other

statistical techniques to solve complex business problems.

● Design and conduct rigorous experiments, A/B tests, and statistical hypothesis tests to

measure the effectiveness of data-driven solutions.

● Communicate complex analytical findings and insights to both technical and non-technical

stakeholders through visualizations, presentations, and reports.

● Stay up-to-date with the latest advancements in data science, machine learning, and related

technologies, and apply them to improve existing processes and methodologies.

● Provide guidance, mentorship, and technical leadership to junior data scientists, fostering a

collaborative and knowledge-sharing culture within the team.

Requirements:

● Bachelor's or advanced degree in Computer Science, Statistics, Mathematics, or a related

quantitative field.

● Minimum of 8 years of professional experience as a Data Scientist, with a proven track record

of delivering impactful data-driven solutions.

● Expertise in machine learning techniques such as regression, classification, clustering, time

series analysis, natural language processing, and recommendation systems

● Proficiency in programming languages such as Python, R, or Scala, along with experience

working with libraries and frameworks like scikit-learn, TensorFlow, PyTorch, or Keras.

● Solid understanding of statistical analysis, experimental design, and hypothesis testing.

● Experience with big data technologies (e.g., Hadoop, Spark) and working with large-scale data

sets.

● Strong data manipulation and SQL skills, along with proficiency in data visualization tools like

Tableau, Power BI, or matplotlib.

● Demonstrated ability to lead and manage complex data science projects, including project

scoping, planning, and execution.

● Excellent problem-solving and critical-thinking skills, with a keen attention to detail and a

passion for tackling challenging analytical problems.

● Strong communication skills, with the ability to translate complex technical concepts into clear

and concise insights for stakeholders at various levels of the organization.