Scimus

Business Development Manager

My day-to-day tasks were: 
- researching industry trends, competitors, and market conditions to stay informed and adapt sales strategies accordingly
- establish, develop and maintain positive customer relationships
- develop and maintain outreach sales processes for LinkedIn, Mail and Upwork
- identifying customers' needs

My learning points:
- failures help you to think out-of-the-box
- persistence is a key
- first - listen, second - help (not sell)

Growth Manager

Translator

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• Conducting research and analyzing the business performance of business partners and fulfilling customers’ needs.
• Create, plan, and deliver presentations on company products.
• Establish, develop and maintain positive business and customer relationships.
• Coordinate with other team members and departments to optimize processes.

Sales Development Specialist

AIESEC

Leading 6 Team Leaders with regular O2Os (40+), department meetings (60 in general), MoSs & KPIs setting and tracking, membership onboarding
Servicing customers before and during their experiences and providing needed standards of quality(improved conversion of quality from 85% to 91%)
Updated servicing flow which helped to get more interaction with customers
Synergy within Sales and Customer Service teams
Sales strategy creation and execution

Head of Customer Experience and Sales

Key responsibilities:
Leading a team of 2 people
Conducting calls and meetings with current accounts(conversion from call to meeting improved from 10.7% to 16.5%)
Generation of leads and market segmentation(93 new leads)
Sales strategy plan execution

Key achievements:
New Sales - 3 new companies
Account management - 5 accounts
Access to the new region

Sales Manager

Business Development Manager at Scimus | Helping businesses to go from Good to Great through automation

Hey! I’m Nelia, your Business Development Manager from Scimus, where I'm dedicated to transforming businesses from good to great through innovative automation solutions. 

With a background in sales, project management, and strategic thinking, I'm swimming in environments that demand creativity and adaptability.

At Scimus, my role involves understanding the market needs, fostering customer relationships, and developing effective sales strategies. Over my 3 years in sales, my experience has honed my skills in identifying customer needs and tailoring solutions that align with their objectives.

I’m always open to discussing how me and Scimus can assist in your business's growth journey. Let’s connect and explore how we can take your business to the next level.

nelia.kaminska@thescimus.com

Nelia Kaminska

Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

I am excited to share a comprehensive resource: Machine Learning, Deep Learning, and Artificial Intelligence Cheat Sheets compiled by Nikhil Yadav from Stanford University and MIT!

This collection of cheat sheets covers key concepts and techniques across machine learning, deep learning, and AI. 

Perfect for students, professionals, and enthusiasts, this document provides quick references and summaries of essential topics in these fields.

Key topics include:
- Probability and Statistics fundamentals
- Supervised and Unsupervised Learning algorithms
- Linear and Logistic Regression
- Support Vector Machines (SVM) and Generative Learning
- Tree-based and Ensemble Methods
- Principal Component Analysis (PCA) and other dimension reduction techniques
- Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
- Reinforcement Learning and Control
- Essential metrics for model evaluation and selection
- Tips and tricks for effective machine learning practice


Enhance your understanding and mastery of machine learning, deep learning, and AI with these detailed cheat sheets!

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li_at

JSESSIONID

Talks about #data, #kindness, #interviewtips, #selfimprovement, and #careertransition

ML, Deep Learning and AI Cheat Sheet

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

I'm excited to share an invaluable resource: the Data Engineer Interview Guide by Simplilearn!

Preparing for a data engineering interview can be daunting, but this comprehensive guide has got you covered. 

Whether you are a fresh graduate or an experienced professional, this document provides a detailed overview of the key questions and answers you can expect in a data engineering interview.

Key topics include:
- Understanding the role and responsibilities of a data engineer
- Differences between data engineers and data scientists
- Key skills required for data engineering, including SQL, Hadoop, and Python
- Essential frameworks and applications for data engineers
- Commonly asked interview questions and how to answer them
- Practical tips for handling job-related crises and real-world scenarios
- Insights into data modelling, ETL processes, and working with big data in cloud environments


Enhance your interview preparation and stand out from the competition with this detailed guide!

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Data Engineering Intro

Excited to share an essential resource: DAX Query Plans by Alberto Ferrari!

This comprehensive paper introduces the performance analysis and optimization of DAX code through the use of DAX query plans. 

Ideal for BI professionals and data analysts, this document provides valuable insights into improving query performance by understanding and optimizing DAX queries.

Key topics include:
- Finding and interpreting DAX query plans
- Differences between logical and physical query plans
- Overview of the formula engine and storage engine
- Basic optimization techniques for DAX queries
- Practical examples using the Contoso and AdventureWorks databases


Enhance your DAX skills and improve query performance with this detailed guide!

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DAX Query Plans

Why data engineers are like the unsung heroes of the tech world – and why you should become one.

Ever wondered what makes the tech world tick?

It’s not just the flashy apps or cutting-edge AI.
It’s the data engineers behind the scenes, making everything work smoothly.

Here’s why they’re the real MVPs:

1. Foundation Builders

Data engineers lay the groundwork for any data-driven project.

Without them, data scientists and analysts would be flying blind.

They create robust data pipelines that ensure seamless data flow – the backbone of every tech solution.

2. Problem Solvers

Every day brings a new challenge.

From fragmented data sources to integrating complex systems, data engineers thrive on solving intricate puzzles.

Their work is crucial for turning raw data into actionable insights.

3. Innovation Enablers

The world’s first AI system for air traffic control?

That’s just one example.

Data engineers make groundbreaking innovations possible by ensuring data is clean, reliable, and ready for analysis.

4. Guardians of Data Integrity

Data is valuable, but only if it’s accurate.

Data engineers ensure quality and consistency, protecting organisations from costly errors.

They’re the gatekeepers of trustworthy data.

5. Educators and Mentors

Building the future doesn’t stop at technology.

Data engineers also shape the next generation of tech talent, sharing their knowledge and experience with aspiring professionals.

So, why should you consider becoming a data engineer?
→ It’s a role that combines creativity and technical skill.
→ There’s an evergrowing demand for data-driven solutions.
→ You’ll be at the forefront of innovation, where every project is an opportunity to make a significant impact.

Join the ranks of these unsung heroes and be part of a community that drives the tech world forward.

Are you ready to build the future?

Let’s discuss why data engineering should be your next step.

Drop a comment below to share your thoughts or ask questions about this exciting field.

The real secret to becoming a data professional isn't technical skills—it's storytelling.

Here's how narrative trumps numbers.

When I first entered the world of data, I was captivated by the endless possibilities that numbers could unlock.

But soon, I realised something crucial: data alone doesn't speak.

It's the story you weave around it that truly makes an impact.

Let me explain how you can harness the power of storytelling in data:

1. Understand Your Audience
  ↳ Tailor your narrative to resonate with the specific needs and interests of your audience.
  Whether you're speaking to executives or data enthusiasts, adjust your story accordingly.

2. Find the Human Element
  ↳ Data is ultimately about people.
  By highlighting the human impact, you make your insights relatable and actionable.

3. Create a Compelling Narrative Arc
  ↳ Like any good story, your data-driven narrative should have a clear beginning, middle, and end.
  Start with the problem, introduce the data, and conclude with actionable insights.

4. Simplify Complex Data
  ↳ Great storytelling involves breaking down complex data into understandable and engaging segments.
  Use visuals and analogies to make your points clear.

5. Inspire Action
  ↳ Your story should lead to a call to action, encouraging your audience to make informed decisions based on your insights.
  
Remember, data storytelling isn't just about presenting facts—it's about driving change.

By mastering the art of storytelling in data, you transform from a mere analyst to an influential data storyteller.

And the best part?

Anyone can learn this skill.

So, are you ready to craft stories that drive data-driven decisions?

Start weaving your narrative today.

Data degrees are great, but here’s why data engineering skills might just be the game-changer you need.

In a world overflowing with data, what’s the biggest challenge?

Turning it into something valuable.

That’s where a data engineer steps in.

Here’s why mastering data engineering skills can outshine a traditional data science degree in today’s job market:

1. Demand for Data Engineers is Soaring

→ Companies need robust data pipelines and clean data before they can even start analysing.

↳ Without data engineers, data scientists can’t do their jobs effectively.

2. HandsOn Problem Solving

→ Data engineering focuses on building and optimising data systems.

↳ You’ll deal with realworld data challenges and provide solutions that have immediate impacts.

3. Versatile Skill Set

→ From coding and database management to understanding cloud services, data engineering equips you with a wide array of skills.

↳ It’s about the whole data ecosystem, not just analysis.

4. Foundation for Advanced Data Exploration

→ Before models can be trained, data must be prepped and organised.

↳ This means your work sets the stage for any advanced data science application.

5. Bridge the Gap Between Theory and Practice

→ While data science often leans towards theoretical aspects, engineering is grounded in practical application.

↳ You’re not just predicting the future; you’re building the road to it.

6. CostEffective Learning

→ You can learn data engineering through online courses, handson projects, and bootcamps.

↳ No need for a hefty degree investment.

7. Innovation at the Core

→ Data engineers are the architects of the digital age, creating systems that allow companies to innovate and grow.

↳ You’re paving the way for future technologies.

So, if you’re weighing options between a degree and diving into data engineering, consider what aligns with the current industry needs.

The world needs more data engineers.

Are you ready to be one?

Engage with me in the comments if you’re considering a career shift or just starting out in data engineering.

What’s your next step?

Excited to share an insightful resource: MLlib: Scalable Machine Learning on Spark by Xiangrui Meng and collaborators!

This comprehensive guide explores the powerful capabilities of MLlib, Spark's scalable machine learning library.

It covers everything from basic algorithms to advanced machine learning techniques, making it an essential resource for data scientists and engineers looking to leverage Spark for large-scale data processing and analysis.

Key topics include:
- Overview of MLlib and its integration with Apache Spark
- Machine learning algorithms: classification, regression, clustering, and collaborative filtering
- Practical examples and implementations of logistic regression, k-means clustering, and ALS
- Performance comparisons and scaling properties of MLlib
- New features and updates in MLlib v1.x
- Integration with other Spark components like Spark SQL, Streaming, and GraphX


Unlock the full potential of machine learning with MLlib on Spark and take your data projects to the next level!

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Why I believe the future of data engineering lies not in new tech but in teaching empathy and communication.

I started in data engineering with the belief that the latest tech would solve every problem.

That mindset shifted.

Here's why: it's not just about the tools.

It's about understanding the humans behind the data.

In my journey from data analyst to data engineering leader, I've learned that the hardest problems aren't technical.

They're human.

When I was tasked with transforming raw data into insights, I quickly realised that many organisations were drowning in fragmented data.

Not because they lacked technology, but because they lacked communication.

→ Data was siloed.

→ Teams didn't talk to each other.

→ Processes were inefficient.

So I focused on:

1. Building Bridges: I brought together diverse teams, ensuring everyone understood the data's value and purpose.

2. Teaching Empathy: I encouraged my team to see data not just as numbers but as stories with human impact.

3. Enhancing Communication: I led efforts to develop clear channels and languages for data discussions, making complex insights accessible to all.

It wasn’t just about algorithms.

It was about collaboration.

About understanding the fears and hopes of those who rely on the system.

When we embrace empathy and communication, we transform data from a static resource into a dynamic force for change.

We give it life.

The future of data engineering won't be about the flashiest tools.

It will be about the human touch.

So here's my challenge to you:

Focus on the human side of data.

Ask more questions.

Listen more.

Let's build a future where data brings us together, not keeps us apart.

What do you think?

How can we make data engineering more human?

Share your thoughts below.

taplio

The Battle

Nelia Kaminska

Dr Emmanuel Ogungbemi

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The Battle

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Dr Emmanuel Ogungbemi

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