Senior Data Scientist

Senior Data Scientist 
Kalasko's hiring a Senior Data scientist to be outsourced for one of our clients,  you will play a crucial role in leveraging data analytics and machine learning techniques to optimize and enhance various aspects of our operations. You will work closely with cross-functional teams to analyze large datasets, extract actionable insights, and develop predictive models to improve decision-making processes and drive efficiency across the organization.

Responsibilities 

Data Collection and Processing:

Collect, clean, and preprocess large volumes of structured and unstructured data from various sources, including telematics systems, sensors, IoT devices, and internal databases.
Develop data pipelines and automation processes to ensure efficient data collection, storage, and retrieval.

Data Analysis and Modeling:

Build the foundational data science analytical culture.
Perform exploratory data analysis (EDA) to identify patterns, trends, and correlations in the data.
Develop and apply statistical and machine learning models, such as regression analysis, classification algorithms, clustering techniques, and time series forecasting, to solve business problems and optimize operational processes.
Collaborate with domain experts and stakeholders to define key performance indicators (KPIs) and metrics for evaluating model performance and business impact.

Predictive Maintenance and Fault Detection:

Develop predictive maintenance models to forecast equipment failures, identify potential issues proactively, and optimize maintenance schedules to minimize downtime and maximize asset reliability.
Implement anomaly detection algorithms to monitor real-time data streams and identify abnormal behavior or malfunctions in truck systems and components.

Optimization and Route Planning:

Develop optimization algorithms and decision support tools to optimize route planning, vehicle routing, and scheduling operations, considering factors such as traffic conditions, weather forecasts, vehicle capacity, and delivery priorities.
Analyze historical data and real-time traffic information to identify opportunities for route optimization, fuel efficiency improvements, and cost savings.

Data Visualization and Reporting:
Create interactive dashboards, reports, and data visualizations to communicate insights and findings to stakeholders, including executives, managers, and frontline staff.
Present analysis results and recommendations in a clear, concise, and actionable manner, using visualizations, charts, and narrative storytelling techniques.

Research and Innovation:

Stay up-to-date with the latest developments in data science, machine learning, and artificial intelligence technologies.
Conduct research and experiments to explore new algorithms, methodologies, and techniques that can be applied to solve specific challenges and improve operational efficiency in the industry.

Qualifications 

- Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- Proven experience (4+ Years) working as a data scientist, machine learning engineer, or analytics professional in a relevant industry, such as logistics, transportation, or supply chain management.
- Proficiency in programming languages and tools commonly used in data science, such as Python, R, SQL, and libraries/frameworks.
- Previous experience working with the following types of data (invoices data, GPS data, Discounts data, credit card data, tolls data, and Documents data)
- Familiarity with fundamental NLP techniques including tokenization, lemmatization, syntactic analysis, and language modeling;
- Solid understanding of fundamental ML principles including clustering, neural networks, and generative models such as GANs;
- Experience with libraries like NLTK, spaCy, and transformers.;
- Ability to work with large volumes of data, perform exploratory analysis, preprocess data, and extract relevant features.
- Strong understanding of statistical analysis, machine learning algorithms, and data mining techniques.
- Experience with big data technologies and platforms, such as Hadoop, Spark, Kafka,
- Experience with cloud computing platforms such as AWS, Google Cloud Platform, or Microsoft Azure.
- Excellent problem-solving skills, critical thinking abilities, and attention to detail.
- Effective communication and collaboration skills, with the ability to work cross-functionally and present complex technical concepts to non-technical stakeholders.
- Creative mindset, curiosity, and a passion for leveraging data-driven insights to drive business outcomes and create value.